diff --git a/pina/egno_data/1.amc b/pina/egno_data/1.amc
new file mode 100644
index 00000000..927a8ad7
--- /dev/null
+++ b/pina/egno_data/1.amc
@@ -0,0 +1,4443 @@
+#!OML:ASF F:\VICON\USERDATA\INSTALL\rory3\rory3.ASF
+:FULLY-SPECIFIED
+:DEGREES
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\ No newline at end of file
diff --git a/pina/egno_data/10.amc b/pina/egno_data/10.amc
new file mode 100644
index 00000000..02ec85f7
--- /dev/null
+++ b/pina/egno_data/10.amc
@@ -0,0 +1,3963 @@
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\ No newline at end of file
diff --git a/pina/egno_data/11.amc b/pina/egno_data/11.amc
new file mode 100644
index 00000000..83f4a003
--- /dev/null
+++ b/pina/egno_data/11.amc
@@ -0,0 +1,4953 @@
+#!OML:ASF F:\VICON\USERDATA\INSTALL\rory3\rory3.ASF
+:FULLY-SPECIFIED
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\ No newline at end of file
diff --git a/pina/egno_data/2.amc b/pina/egno_data/2.amc
new file mode 100644
index 00000000..7a6f3196
--- /dev/null
+++ b/pina/egno_data/2.amc
@@ -0,0 +1,3903 @@
+#!OML:ASF F:\VICON\USERDATA\INSTALL\rory3\rory3.ASF
+:FULLY-SPECIFIED
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\ No newline at end of file
diff --git a/pina/egno_data/3.amc b/pina/egno_data/3.amc
new file mode 100644
index 00000000..545c6105
--- /dev/null
+++ b/pina/egno_data/3.amc
@@ -0,0 +1,3843 @@
+#!OML:ASF F:\VICON\USERDATA\INSTALL\rory3\rory3.ASF
+:FULLY-SPECIFIED
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\ No newline at end of file
diff --git a/pina/egno_data/4.amc b/pina/egno_data/4.amc
new file mode 100644
index 00000000..58b9a039
--- /dev/null
+++ b/pina/egno_data/4.amc
@@ -0,0 +1,4113 @@
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\ No newline at end of file
diff --git a/pina/egno_data/5.amc b/pina/egno_data/5.amc
new file mode 100644
index 00000000..0e80df4f
--- /dev/null
+++ b/pina/egno_data/5.amc
@@ -0,0 +1,4293 @@
+#!OML:ASF F:\VICON\USERDATA\INSTALL\rory3\rory3.ASF
+:FULLY-SPECIFIED
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\ No newline at end of file
diff --git a/pina/egno_data/6.amc b/pina/egno_data/6.amc
new file mode 100644
index 00000000..88dba96e
--- /dev/null
+++ b/pina/egno_data/6.amc
@@ -0,0 +1,4233 @@
+#!OML:ASF F:\VICON\USERDATA\INSTALL\rory3\rory3.ASF
+:FULLY-SPECIFIED
+:DEGREES
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\ No newline at end of file
diff --git a/pina/egno_data/7.amc b/pina/egno_data/7.amc
new file mode 100644
index 00000000..8fc72b49
--- /dev/null
+++ b/pina/egno_data/7.amc
@@ -0,0 +1,4143 @@
+#!OML:ASF F:\VICON\USERDATA\INSTALL\rory3\rory3.ASF
+:FULLY-SPECIFIED
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\ No newline at end of file
diff --git a/pina/egno_data/8.amc b/pina/egno_data/8.amc
new file mode 100644
index 00000000..9258ebf8
--- /dev/null
+++ b/pina/egno_data/8.amc
@@ -0,0 +1,3843 @@
+#!OML:ASF F:\VICON\USERDATA\INSTALL\rory3\rory3.ASF
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\ No newline at end of file
diff --git a/pina/egno_data/9.amc b/pina/egno_data/9.amc
new file mode 100644
index 00000000..fe0df718
--- /dev/null
+++ b/pina/egno_data/9.amc
@@ -0,0 +1,4563 @@
+#!OML:ASF F:\VICON\USERDATA\INSTALL\rory3\rory3.ASF
+:FULLY-SPECIFIED
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+thorax -7.17866 -1.21683 2.10709
+lowerneck -3.7042 1.5467 -1.88595
+upperneck -12.4686 2.23442 1.56549
+head -3.41755 1.13824 0.794694
+rclavicle 1.45113e-014 -2.46493e-014
+rhumerus -51.7756 7.7669 -86.9811
+rradius 86.5842
+rwrist 3.00018
+rhand -39.9841 35.5427
+rfingers 7.12502
+rthumb -12.9525 5.96469
+lclavicle 1.45113e-014 -2.46493e-014
+lhumerus -17.2737 -14.321 82.7962
+lradius 113.884
+lwrist 30.6645
+lhand -34.0279 14.2715
+lfingers 7.12502
+lthumb -7.20651 44.1288
+rfemur -33.9991 -4.97241 12.7585
+rtibia 109.083
+rfoot 29.135 -2.94452
+rtoes 11.9234
+lfemur -4.10972 -5.61072 -30.9132
+ltibia 33.5568
+lfoot -12.5341 -1.7858
+ltoes -30.2311
+149
+root 0.702616 17.5582 47.3947 3.72888 -1.35081 6.83562
+lowerback 15.6402 -1.39789 -7.58889
+upperback 1.81107 -3.30658 -2.30283
+thorax -6.54377 -1.43976 2.51334
+lowerneck -3.09605 1.67274 -2.17743
+upperneck -12.8028 2.37552 1.3728
+head -3.65306 1.22496 0.775197
+rclavicle 4.53229e-014 2.18663e-014
+rhumerus -52.8242 7.69314 -86.8733
+rradius 87.1544
+rwrist 2.82144
+rhand -39.8948 35.9123
+rfingers 7.12502
+rthumb -12.8663 6.32885
+lclavicle 4.53229e-014 2.18663e-014
+lhumerus -16.655 -14.2194 82.6513
+lradius 114.666
+lwrist 31.6713
+lhand -33.7718 11.1732
+lfingers 7.12502
+lthumb -6.95926 41.0388
+rfemur -35.4297 -4.77143 12.7612
+rtibia 107.196
+rfoot 27.5435 -2.45683
+rtoes 11.896
+lfemur -1.39512 -5.38048 -31.3734
+ltibia 31.9811
+lfoot -10.4544 -2.71861
+ltoes -31.5828
+150
+root 0.680392 17.6482 47.9053 3.68314 -1.11522 7.3962
+lowerback 15.6774 -1.70175 -8.29324
+upperback 1.54224 -3.84446 -2.22597
+thorax -6.75246 -1.6653 3.10127
+lowerneck -2.74458 1.84308 -2.12217
+upperneck -12.7048 2.55145 0.941178
+head -3.67877 1.32287 0.616538
+rclavicle -5.42682e-014 -1.47101e-014
+rhumerus -54.1594 7.83667 -86.8991
+rradius 87.6112
+rwrist 2.48727
+rhand -39.7832 35.9269
+rfingers 7.12502
+rthumb -12.7587 6.33674
+lclavicle -5.42682e-014 -1.47101e-014
+lhumerus -16.4774 -14.036 82.6521
+lradius 115.411
+lwrist 32.9506
+lhand -33.1733 10.0258
+lfingers 7.12502
+lthumb -6.38148 39.9097
+rfemur -37.2489 -4.68337 12.6857
+rtibia 105.054
+rfoot 26.5087 -1.75741
+rtoes 10.6756
+lfemur 1.16386 -5.13669 -31.8618
+ltibia 29.828
+lfoot -7.36878 -3.94352
+ltoes -31.1307
+151
+root 0.663249 17.7575 48.4 3.89704 -0.883565 7.97568
+lowerback 15.2472 -2.08035 -8.99008
+upperback 1.6813 -4.39931 -2.19503
+thorax -6.2483 -1.94363 3.59807
+lowerneck -3.35955 2.02727 -1.87924
+upperneck -12.6485 2.76217 0.4808
+head -3.53414 1.43264 0.424478
+rclavicle 3.35946e-014 3.18055e-015
+rhumerus -54.4249 8.03077 -86.8958
+rradius 87.8723
+rwrist 1.92624
+rhand -39.459 36.3007
+rfingers 7.12502
+rthumb -12.4462 6.69129
+lclavicle 3.35946e-014 3.18055e-015
+lhumerus -15.594 -13.8957 82.4352
+lradius 116.11
+lwrist 33.7896
+lhand -33.5556 9.34105
+lfingers 7.12502
+lthumb -6.75054 39.2134
+rfemur -39.2568 -4.5873 12.6147
+rtibia 102.622
+rfoot 26.119 -0.860671
+rtoes 12.6673
+lfemur 3.29279 -4.9338 -32.2783
+ltibia 27.5936
+lfoot -3.69832 -4.98894
+ltoes -29.4626
+152
+root 0.64945 17.8678 48.9782 3.63743 -0.798607 6.97284
+lowerback 16.0966 -2.50877 -7.73497
+upperback 0.779746 -4.88561 -1.92316
+thorax -7.71757 -2.11903 3.37152
+lowerneck -2.65498 2.21833 -1.68999
+upperneck -11.7917 2.97903 0.208641
+head -3.35847 1.53709 0.295368
+rclavicle 3.56322e-014 5.56597e-015
+rhumerus -57.0809 8.2699 -87.2908
+rradius 88.3879
+rwrist 1.54868
+rhand -39.3944 36.7179
+rfingers 7.12502
+rthumb -12.3839 7.10475
+lclavicle 3.56322e-014 5.56597e-015
+lhumerus -17.4671 -14.0867 82.7999
+lradius 117.294
+lwrist 35.2284
+lhand -34.2744 9.45175
+lfingers 7.12502
+lthumb -7.44442 39.3008
+rfemur -39.5855 -3.8343 14.2277
+rtibia 99.188
+rfoot 25.1532 -0.902738
+rtoes 9.44364
+lfemur 6.35343 -4.38348 -31.0586
+ltibia 25.2203
+lfoot -0.180973 -5.84537
+ltoes -28.2159
\ No newline at end of file
diff --git a/pina/egno_data/amc_parser.py b/pina/egno_data/amc_parser.py
new file mode 100644
index 00000000..73e2f80d
--- /dev/null
+++ b/pina/egno_data/amc_parser.py
@@ -0,0 +1,262 @@
+# From official implementation of the Equivariant Graph Neural Operator.
+
+import numpy as np
+import matplotlib.pyplot as plt
+from transforms3d.euler import euler2mat
+from mpl_toolkits.mplot3d import Axes3D
+
+
+class Joint:
+    def __init__(self, name, direction, length, axis, dof, limits):
+        """
+    Definition of basic joint. The joint also contains the information of the
+    bone between it's parent joint and itself. Refer
+    [here](https://research.cs.wisc.edu/graphics/Courses/cs-838-1999/Jeff/ASF-AMC.html)
+    for detailed description for asf files.
+    Parameter
+    ---------
+    name: Name of the joint defined in the asf file. There should always be one
+    root joint. String.
+    direction: Default direction of the joint(bone). The motions are all defined
+    based on this default pose.
+    length: Length of the bone.
+    axis: Axis of rotation for the bone.
+    dof: Degree of freedom. Specifies the number of motion channels and in what
+    order they appear in the AMC file.
+    limits: Limits on each of the channels in the dof specification
+    """
+        self.name = name
+        self.direction = np.reshape(direction, [3, 1])
+        self.length = length
+        axis = np.deg2rad(axis)
+        self.C = euler2mat(*axis)
+        self.Cinv = np.linalg.inv(self.C)
+        self.limits = np.zeros([3, 2])
+        for lm, nm in zip(limits, dof):
+            if nm == 'rx':
+                self.limits[0] = lm
+            elif nm == 'ry':
+                self.limits[1] = lm
+            else:
+                self.limits[2] = lm
+        self.parent = None
+        self.children = []
+        self.coordinate = None
+        self.matrix = None
+
+    def set_motion(self, motion):
+        if self.name == 'root':
+            self.coordinate = np.reshape(np.array(motion['root'][:3]), [3, 1])
+            rotation = np.deg2rad(motion['root'][3:])
+            self.matrix = self.C.dot(euler2mat(*rotation)).dot(self.Cinv)
+        else:
+            idx = 0
+            rotation = np.zeros(3)
+            for axis, lm in enumerate(self.limits):
+                if not np.array_equal(lm, np.zeros(2)):
+                    rotation[axis] = motion[self.name][idx]
+                    idx += 1
+            rotation = np.deg2rad(rotation)
+            self.matrix = self.parent.matrix.dot(self.C).dot(euler2mat(*rotation)).dot(self.Cinv)
+            self.coordinate = self.parent.coordinate + self.length * self.matrix.dot(self.direction)
+        for child in self.children:
+            child.set_motion(motion)
+
+    def get_name_to_idx(self):
+        joints = self.to_dict()
+        name_to_idx = {}
+        for idx, joint in enumerate(joints.values()):
+            assert joint.name not in name_to_idx
+            name_to_idx[joint.name] = idx
+        self.name_to_idx = name_to_idx
+
+    def output_edges(self):
+        joints = self.to_dict()
+        name_to_idx = self.name_to_idx
+        edges = []
+        for idx, joint in enumerate(joints.values()):
+            child = joint
+            if child.parent is not None:
+                parent = child.parent
+                edges.append([name_to_idx[child.name], name_to_idx[parent.name]])
+        return edges
+
+    def output_coord(self):
+        N = len(self.name_to_idx)
+        X = np.zeros((N, 3))
+        joints = self.to_dict()
+        name_to_idx = self.name_to_idx
+        for idx, joint in enumerate(joints.values()):
+            X[name_to_idx[joint.name]] = joint.coordinate.reshape(-1)
+        return X
+
+    def draw(self):
+        joints = self.to_dict()
+        fig = plt.figure()
+        ax = Axes3D(fig)
+
+        ax.set_xlim3d(-50, 10)
+        ax.set_ylim3d(-20, 40)
+        ax.set_zlim3d(-20, 40)
+
+        xs, ys, zs = [], [], []
+        for joint in joints.values():
+            xs.append(joint.coordinate[0, 0])
+            ys.append(joint.coordinate[1, 0])
+            zs.append(joint.coordinate[2, 0])
+        plt.plot(zs, xs, ys, 'b.')
+
+        for joint in joints.values():
+            child = joint
+            if child.parent is not None:
+                parent = child.parent
+                xs = [child.coordinate[0, 0], parent.coordinate[0, 0]]
+                ys = [child.coordinate[1, 0], parent.coordinate[1, 0]]
+                zs = [child.coordinate[2, 0], parent.coordinate[2, 0]]
+                plt.plot(zs, xs, ys, 'r')
+        plt.show()
+
+    def to_dict(self):
+        ret = {self.name: self}
+        for child in self.children:
+            ret.update(child.to_dict())
+        return ret
+
+    def pretty_print(self):
+        print('===================================')
+        print('joint: %s' % self.name)
+        print('direction:')
+        print(self.direction)
+        print('limits:', self.limits)
+        print('parent:', self.parent)
+        print('children:', self.children)
+
+
+def read_line(stream, idx):
+    if idx >= len(stream):
+        return None, idx
+    line = stream[idx].strip().split()
+    idx += 1
+    return line, idx
+
+
+def parse_asf(file_path):
+    '''read joint data only'''
+    with open(file_path) as f:
+        content = f.read().splitlines()
+
+    for idx, line in enumerate(content):
+        # meta infomation is ignored
+        if line == ':bonedata':
+            content = content[idx + 1:]
+            break
+
+    # read joints
+    joints = {'root': Joint('root', np.zeros(3), 0, np.zeros(3), [], [])}
+    idx = 0
+    while True:
+        # the order of each section is hard-coded
+
+        line, idx = read_line(content, idx)
+
+        if line[0] == ':hierarchy':
+            break
+
+        assert line[0] == 'begin'
+
+        line, idx = read_line(content, idx)
+        assert line[0] == 'id'
+
+        line, idx = read_line(content, idx)
+        assert line[0] == 'name'
+        name = line[1]
+
+        line, idx = read_line(content, idx)
+        assert line[0] == 'direction'
+        direction = np.array([float(axis) for axis in line[1:]])
+
+        # skip length
+        line, idx = read_line(content, idx)
+        assert line[0] == 'length'
+        length = float(line[1])
+
+        line, idx = read_line(content, idx)
+        assert line[0] == 'axis'
+        assert line[4] == 'XYZ'
+
+        axis = np.array([float(axis) for axis in line[1:-1]])
+
+        dof = []
+        limits = []
+
+        line, idx = read_line(content, idx)
+        if line[0] == 'dof':
+            dof = line[1:]
+            for i in range(len(dof)):
+                line, idx = read_line(content, idx)
+                if i == 0:
+                    assert line[0] == 'limits'
+                    line = line[1:]
+                assert len(line) == 2
+                mini = float(line[0][1:])
+                maxi = float(line[1][:-1])
+                limits.append((mini, maxi))
+
+            line, idx = read_line(content, idx)
+
+        assert line[0] == 'end'
+        joints[name] = Joint(
+            name,
+            direction,
+            length,
+            axis,
+            dof,
+            limits
+        )
+
+    # read hierarchy
+    assert line[0] == ':hierarchy'
+
+    line, idx = read_line(content, idx)
+
+    assert line[0] == 'begin'
+
+    while True:
+        line, idx = read_line(content, idx)
+        if line[0] == 'end':
+            break
+        assert len(line) >= 2
+        for joint_name in line[1:]:
+            joints[line[0]].children.append(joints[joint_name])
+        for nm in line[1:]:
+            joints[nm].parent = joints[line[0]]
+
+    return joints
+
+
+def parse_amc(file_path):
+    with open(file_path) as f:
+        content = f.read().splitlines()
+
+    for idx, line in enumerate(content):
+        if line == ':DEGREES':
+            content = content[idx + 1:]
+            break
+
+    frames = []
+    idx = 0
+    line, idx = read_line(content, idx)
+    assert line[0].isnumeric(), line
+    EOF = False
+    while not EOF:
+        joint_degree = {}
+        while True:
+            line, idx = read_line(content, idx)
+            if line is None:
+                EOF = True
+                break
+            if line[0].isnumeric():
+                break
+            joint_degree[line[0]] = [float(deg) for deg in line[1:]]
+        frames.append(joint_degree)
+    return frames
\ No newline at end of file
diff --git a/pina/egno_data/egno_torch_train.ipynb b/pina/egno_data/egno_torch_train.ipynb
new file mode 100644
index 00000000..799a3d75
--- /dev/null
+++ b/pina/egno_data/egno_torch_train.ipynb
@@ -0,0 +1,4434 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "775c0d19",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The autoreload extension is already loaded. To reload it, use:\n",
+      "  %reload_ext autoreload\n"
+     ]
+    }
+   ],
+   "source": [
+    "%load_ext autoreload"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "aa4783a4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%autoreload 1\n",
+    "%aimport pina.model.block.egno_block\n",
+    "\n",
+    "from pina.model.block.egno_block import EquivariantGraphNeuralOperatorBlock\n",
+    "\n",
+    "from torch_geometric.data import Dataset, Data\n",
+    "import amc_parser as amc\n",
+    "import numpy as np\n",
+    "import torch\n",
+    "import torch.optim as optim\n",
+    "from transforms3d.euler import euler2mat\n",
+    "from mpl_toolkits.mplot3d import Axes3D"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "c18552fc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Data\n",
+    "def Generate_input(timesteps):\n",
+    "    asf_name = 'run.asf'\n",
+    "    joints = amc.parse_asf(asf_name)\n",
+    "    joints['root'].get_name_to_idx()\n",
+    "    edge_index = joints['root'].output_edges()\n",
+    "    edge_index = torch.tensor(edge_index).T  # Shape: [2, num_edges]\n",
+    "\n",
+    "    train_case_id = [1, 2, 5, 6, 10]\n",
+    "    val_case_id = [0, 4, 9]\n",
+    "    test_case_id = [3, 7, 8]\n",
+    "\n",
+    "    train_mapping = {}\n",
+    "    for i in train_case_id:\n",
+    "        sampled = np.random.choice(np.arange(100), size=48, replace=False)\n",
+    "        train_mapping[i] = sampled\n",
+    "\n",
+    "    all_graphs = {}\n",
+    "    input_data = []\n",
+    "    output_data = []\n",
+    "\n",
+    "    for i in train_case_id:\n",
+    "        amc_name = f'{i}.amc'\n",
+    "        motions = amc.parse_amc(amc_name)\n",
+    "        graphs = []\n",
+    "\n",
+    "        for time, motion in enumerate(motions):\n",
+    "            joints['root'].set_motion(motion)\n",
+    "            node_coords = joints['root'].output_coord()\n",
+    "            # Convert to tensor and ensure correct shape [num_nodes, 3]\n",
+    "            pos = torch.tensor(node_coords, dtype=torch.float32)\n",
+    "            \n",
+    "            # Create a dictionary with the graph data\n",
+    "            graph_data = {\n",
+    "                'edge_index': edge_index,\n",
+    "                'pos': pos,\n",
+    "                'time': torch.tensor(time, dtype=torch.float32)\n",
+    "            }\n",
+    "            graphs.append(graph_data)\n",
+    "\n",
+    "        for index in range(0, len(graphs)-1, 1):\n",
+    "            graphs[index]['velo'] = graphs[index+1]['pos']-graphs[index]['pos']\n",
+    "        # duplicate last for finite differences\n",
+    "        graphs[-1]['velo'] = graphs[-2]['velo']\n",
+    "            \n",
+    "        all_graphs[i] = graphs\n",
+    "\n",
+    "        for step in train_mapping[i]:\n",
+    "            # Input: single graph\n",
+    "            input_data.append(graphs[step])\n",
+    "\n",
+    "            # Out: num timesteps graphs\n",
+    "            future_graphs = []\n",
+    "            for jump in range(1, timesteps + 1):\n",
+    "                future_graphs.append(graphs[step + jump])\n",
+    "            output_data.append(future_graphs)\n",
+    "\n",
+    "    return input_data, output_data\n",
+    "\n",
+    "class MotionDataset(Dataset):\n",
+    "    def __init__(self, inputs, outputs):\n",
+    "        self.inputs = inputs\n",
+    "        self.outputs = outputs\n",
+    "        \n",
+    "    def __len__(self):\n",
+    "        return len(self.inputs)\n",
+    "    \n",
+    "    def __getitem__(self, idx):\n",
+    "        input_graph = self.inputs[idx]\n",
+    "        output_graphs = self.outputs[idx]\n",
+    "        \n",
+    "        # Stack positions from output sequence [timesteps, num_nodes, 3]\n",
+    "        output_pos = torch.stack([g['pos'] for g in output_graphs])\n",
+    "        output_pos = output_pos.transpose(0,1)\n",
+    "        \n",
+    "        data = Data(pos=input_graph['pos'], edge_index=input_graph['edge_index'])\n",
+    "\n",
+    "        data.output_pos = output_pos\n",
+    "        data.velo = input_graph['velo']\n",
+    "\n",
+    "        return data\n",
+    "\n",
+    "timesteps = 5\n",
+    "input_data, output_data = Generate_input(timesteps)\n",
+    "dataset = MotionDataset(input_data, output_data)\n",
+    "\n",
+    "\n",
+    "from torch_geometric.loader import DataLoader\n",
+    "dataloader = DataLoader(\n",
+    "    dataset, \n",
+    "    batch_size=12,\n",
+    "    shuffle=True\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "120c63ed",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "EquivariantGraphNeuralOperatorBlock(\n",
+      "  (layers): ModuleList(\n",
+      "    (0-5): 6 x TemporalConvolutionLayer(\n",
+      "      (M_H): M_H()\n",
+      "      (M_Z): M_Z()\n",
+      "    )\n",
+      "  )\n",
+      "  (egnn_layer): EnEquivariantNetworkBlock()\n",
+      ")\n",
+      "Number of parameters: 40148\n"
+     ]
+    }
+   ],
+   "source": [
+    "def eq_activation(layer):\n",
+    "    norm = torch.norm(layer, dim=-1, keepdim=True)\n",
+    "    return torch.tanh(norm) * layer\n",
+    "\n",
+    "# Define Model\n",
+    "model = EquivariantGraphNeuralOperatorBlock(n_layers = 6,\n",
+    "        time_discretizations = timesteps,\n",
+    "        num_scalar_features = 0,\n",
+    "        num_vec_features = 2,\n",
+    "        num_edge_features = 1,\n",
+    "        embedding_dim = 32,\n",
+    "        n_nodes = 31*12,\n",
+    "        n_modes= 2,\n",
+    "        eq_activation = eq_activation,\n",
+    "        scalar_feature_activation = torch.relu)\n",
+    "\n",
+    "import warnings\n",
+    "warnings.filterwarnings(\n",
+    "    \"ignore\",\n",
+    "    message=\".*type_params.*typing._eval_type.*\",\n",
+    "    category=DeprecationWarning\n",
+    ")\n",
+    "\n",
+    "print(model)\n",
+    "print(f\"Number of parameters: {sum(p.numel() for p in model.parameters() if p.requires_grad)}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "222e07ae",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "learning_rate = 1e-4\n",
+    "batch_size=12\n",
+    "epochs=1000\n",
+    "\n",
+    "def loss_func(output, target):\n",
+    "    loss = torch.nn.functional.mse_loss(output, target)\n",
+    "    return loss\n",
+    "optimizer = optim.Adam(params = model.parameters(), lr=learning_rate, weight_decay=1e-10)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "4e974de6",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1\n",
+      "-------------------------------\n",
+      "loss: 37.554684  [   12/  240]\n",
+      "loss: 30.920204  [  132/  240]\n",
+      "Epoch 2\n",
+      "-------------------------------\n",
+      "loss: 47.487568  [   12/  240]\n",
+      "loss: 30.151567  [  132/  240]\n",
+      "Epoch 3\n",
+      "-------------------------------\n",
+      "loss: 22.492134  [   12/  240]\n",
+      "loss: 26.493778  [  132/  240]\n",
+      "Epoch 4\n",
+      "-------------------------------\n",
+      "loss: 32.487076  [   12/  240]\n",
+      "loss: 23.088577  [  132/  240]\n",
+      "Epoch 5\n",
+      "-------------------------------\n",
+      "loss: 16.959282  [   12/  240]\n",
+      "loss: 7.117496  [  132/  240]\n",
+      "Epoch 6\n",
+      "-------------------------------\n",
+      "loss: 6.247176  [   12/  240]\n",
+      "loss: 2.308407  [  132/  240]\n",
+      "Epoch 7\n",
+      "-------------------------------\n",
+      "loss: 2.809761  [   12/  240]\n",
+      "loss: 67.785149  [  132/  240]\n",
+      "Epoch 8\n",
+      "-------------------------------\n",
+      "loss: 20.318398  [   12/  240]\n",
+      "loss: 2.551324  [  132/  240]\n",
+      "Epoch 9\n",
+      "-------------------------------\n",
+      "loss: 2.166289  [   12/  240]\n",
+      "loss: 2.211941  [  132/  240]\n",
+      "Epoch 10\n",
+      "-------------------------------\n",
+      "loss: 1.892744  [   12/  240]\n",
+      "loss: 2.437692  [  132/  240]\n",
+      "Epoch 11\n",
+      "-------------------------------\n",
+      "loss: 1.960333  [   12/  240]\n",
+      "loss: 2.081563  [  132/  240]\n",
+      "Epoch 12\n",
+      "-------------------------------\n",
+      "loss: 1.872502  [   12/  240]\n",
+      "loss: 20.075674  [  132/  240]\n",
+      "Epoch 13\n",
+      "-------------------------------\n",
+      "loss: 1.909332  [   12/  240]\n",
+      "loss: 26.014687  [  132/  240]\n",
+      "Epoch 14\n",
+      "-------------------------------\n",
+      "loss: 1.958294  [   12/  240]\n",
+      "loss: 1.957295  [  132/  240]\n",
+      "Epoch 15\n",
+      "-------------------------------\n",
+      "loss: 63.537148  [   12/  240]\n",
+      "loss: 25.779951  [  132/  240]\n",
+      "Epoch 16\n",
+      "-------------------------------\n",
+      "loss: 25.918077  [   12/  240]\n",
+      "loss: 20.001682  [  132/  240]\n",
+      "Epoch 17\n",
+      "-------------------------------\n",
+      "loss: 19.993879  [   12/  240]\n",
+      "loss: 1.664531  [  132/  240]\n",
+      "Epoch 18\n",
+      "-------------------------------\n",
+      "loss: 1.783443  [   12/  240]\n",
+      "loss: 1.863501  [  132/  240]\n",
+      "Epoch 19\n",
+      "-------------------------------\n",
+      "loss: 1.719915  [   12/  240]\n",
+      "loss: 1.813515  [  132/  240]\n",
+      "Epoch 20\n",
+      "-------------------------------\n",
+      "loss: 25.801714  [   12/  240]\n",
+      "loss: 1.703095  [  132/  240]\n",
+      "Epoch 21\n",
+      "-------------------------------\n",
+      "loss: 1.721941  [   12/  240]\n",
+      "loss: 1.655581  [  132/  240]\n",
+      "Epoch 22\n",
+      "-------------------------------\n",
+      "loss: 1.599365  [   12/  240]\n",
+      "loss: 1.719045  [  132/  240]\n",
+      "Epoch 23\n",
+      "-------------------------------\n",
+      "loss: 1.698172  [   12/  240]\n",
+      "loss: 1.679068  [  132/  240]\n",
+      "Epoch 24\n",
+      "-------------------------------\n",
+      "loss: 1.611249  [   12/  240]\n",
+      "loss: 1.580499  [  132/  240]\n",
+      "Epoch 25\n",
+      "-------------------------------\n",
+      "loss: 1.652224  [   12/  240]\n",
+      "loss: 1.562659  [  132/  240]\n",
+      "Epoch 26\n",
+      "-------------------------------\n",
+      "loss: 1.610929  [   12/  240]\n",
+      "loss: 1.536917  [  132/  240]\n",
+      "Epoch 27\n",
+      "-------------------------------\n",
+      "loss: 1.511346  [   12/  240]\n",
+      "loss: 1.632743  [  132/  240]\n",
+      "Epoch 28\n",
+      "-------------------------------\n",
+      "loss: 1.502303  [   12/  240]\n",
+      "loss: 1.570243  [  132/  240]\n",
+      "Epoch 29\n",
+      "-------------------------------\n",
+      "loss: 57.329605  [   12/  240]\n",
+      "loss: 1.663879  [  132/  240]\n",
+      "Epoch 30\n",
+      "-------------------------------\n",
+      "loss: 1.567793  [   12/  240]\n",
+      "loss: 1.462272  [  132/  240]\n",
+      "Epoch 31\n",
+      "-------------------------------\n",
+      "loss: 1.632871  [   12/  240]\n",
+      "loss: 1.564178  [  132/  240]\n",
+      "Epoch 32\n",
+      "-------------------------------\n",
+      "loss: 1.490643  [   12/  240]\n",
+      "loss: 1.542738  [  132/  240]\n",
+      "Epoch 33\n",
+      "-------------------------------\n",
+      "loss: 1.537580  [   12/  240]\n",
+      "loss: 19.716333  [  132/  240]\n",
+      "Epoch 34\n",
+      "-------------------------------\n",
+      "loss: 1.428295  [   12/  240]\n",
+      "loss: 1.496527  [  132/  240]\n",
+      "Epoch 35\n",
+      "-------------------------------\n",
+      "loss: 1.520471  [   12/  240]\n",
+      "loss: 1.480056  [  132/  240]\n",
+      "Epoch 36\n",
+      "-------------------------------\n",
+      "loss: 55.109108  [   12/  240]\n",
+      "loss: 1.567996  [  132/  240]\n",
+      "Epoch 37\n",
+      "-------------------------------\n",
+      "loss: 1.484132  [   12/  240]\n",
+      "loss: 1.418701  [  132/  240]\n",
+      "Epoch 38\n",
+      "-------------------------------\n",
+      "loss: 1.429944  [   12/  240]\n",
+      "loss: 1.564583  [  132/  240]\n",
+      "Epoch 39\n",
+      "-------------------------------\n",
+      "loss: 1.446725  [   12/  240]\n",
+      "loss: 1.442824  [  132/  240]\n",
+      "Epoch 40\n",
+      "-------------------------------\n",
+      "loss: 1.392206  [   12/  240]\n",
+      "loss: 1.451203  [  132/  240]\n",
+      "Epoch 41\n",
+      "-------------------------------\n",
+      "loss: 1.413114  [   12/  240]\n",
+      "loss: 1.510056  [  132/  240]\n",
+      "Epoch 42\n",
+      "-------------------------------\n",
+      "loss: 1.375942  [   12/  240]\n",
+      "loss: 25.516422  [  132/  240]\n",
+      "Epoch 43\n",
+      "-------------------------------\n",
+      "loss: 1.456882  [   12/  240]\n",
+      "loss: 1.406736  [  132/  240]\n",
+      "Epoch 44\n",
+      "-------------------------------\n",
+      "loss: 1.403996  [   12/  240]\n",
+      "loss: 1.364769  [  132/  240]\n",
+      "Epoch 45\n",
+      "-------------------------------\n",
+      "loss: 1.394848  [   12/  240]\n",
+      "loss: 1.296159  [  132/  240]\n",
+      "Epoch 46\n",
+      "-------------------------------\n",
+      "loss: 1.390293  [   12/  240]\n",
+      "loss: 19.699591  [  132/  240]\n",
+      "Epoch 47\n",
+      "-------------------------------\n",
+      "loss: 1.357590  [   12/  240]\n",
+      "loss: 1.343884  [  132/  240]\n",
+      "Epoch 48\n",
+      "-------------------------------\n",
+      "loss: 19.631189  [   12/  240]\n",
+      "loss: 1.375856  [  132/  240]\n",
+      "Epoch 49\n",
+      "-------------------------------\n",
+      "loss: 1.383000  [   12/  240]\n",
+      "loss: 1.360434  [  132/  240]\n",
+      "Epoch 50\n",
+      "-------------------------------\n",
+      "loss: 1.290901  [   12/  240]\n",
+      "loss: 25.484680  [  132/  240]\n",
+      "Epoch 51\n",
+      "-------------------------------\n",
+      "loss: 1.355159  [   12/  240]\n",
+      "loss: 1.312590  [  132/  240]\n",
+      "Epoch 52\n",
+      "-------------------------------\n",
+      "loss: 1.319447  [   12/  240]\n",
+      "loss: 1.316957  [  132/  240]\n",
+      "Epoch 53\n",
+      "-------------------------------\n",
+      "loss: 1.376197  [   12/  240]\n",
+      "loss: 1.254371  [  132/  240]\n",
+      "Epoch 54\n",
+      "-------------------------------\n",
+      "loss: 1.294272  [   12/  240]\n",
+      "loss: 25.467916  [  132/  240]\n",
+      "Epoch 55\n",
+      "-------------------------------\n",
+      "loss: 1.273895  [   12/  240]\n",
+      "loss: 1.261702  [  132/  240]\n",
+      "Epoch 56\n",
+      "-------------------------------\n",
+      "loss: 1.317518  [   12/  240]\n",
+      "loss: 1.309218  [  132/  240]\n",
+      "Epoch 57\n",
+      "-------------------------------\n",
+      "loss: 25.411329  [   12/  240]\n",
+      "loss: 1.305367  [  132/  240]\n",
+      "Epoch 58\n",
+      "-------------------------------\n",
+      "loss: 1.255269  [   12/  240]\n",
+      "loss: 1.276707  [  132/  240]\n",
+      "Epoch 59\n",
+      "-------------------------------\n",
+      "loss: 1.315913  [   12/  240]\n",
+      "loss: 1.261708  [  132/  240]\n",
+      "Epoch 60\n",
+      "-------------------------------\n",
+      "loss: 1.284496  [   12/  240]\n",
+      "loss: 1.289250  [  132/  240]\n",
+      "Epoch 61\n",
+      "-------------------------------\n",
+      "loss: 1.243080  [   12/  240]\n",
+      "loss: 1.211862  [  132/  240]\n",
+      "Epoch 62\n",
+      "-------------------------------\n",
+      "loss: 1.203654  [   12/  240]\n",
+      "loss: 19.489752  [  132/  240]\n",
+      "Epoch 63\n",
+      "-------------------------------\n",
+      "loss: 1.251509  [   12/  240]\n",
+      "loss: 39.854473  [  132/  240]\n",
+      "Epoch 64\n",
+      "-------------------------------\n",
+      "loss: 1.183620  [   12/  240]\n",
+      "loss: 1.229839  [  132/  240]\n",
+      "Epoch 65\n",
+      "-------------------------------\n",
+      "loss: 1.242401  [   12/  240]\n",
+      "loss: 1.229451  [  132/  240]\n",
+      "Epoch 66\n",
+      "-------------------------------\n",
+      "loss: 1.158059  [   12/  240]\n",
+      "loss: 25.282280  [  132/  240]\n",
+      "Epoch 67\n",
+      "-------------------------------\n",
+      "loss: 1.202221  [   12/  240]\n",
+      "loss: 43.574066  [  132/  240]\n",
+      "Epoch 68\n",
+      "-------------------------------\n",
+      "loss: 25.401834  [   12/  240]\n",
+      "loss: 1.152368  [  132/  240]\n",
+      "Epoch 69\n",
+      "-------------------------------\n",
+      "loss: 1.099160  [   12/  240]\n",
+      "loss: 1.139481  [  132/  240]\n",
+      "Epoch 70\n",
+      "-------------------------------\n",
+      "loss: 1.096242  [   12/  240]\n",
+      "loss: 1.115820  [  132/  240]\n",
+      "Epoch 71\n",
+      "-------------------------------\n",
+      "loss: 1.100945  [   12/  240]\n",
+      "loss: 1.090109  [  132/  240]\n",
+      "Epoch 72\n",
+      "-------------------------------\n",
+      "loss: 1.143234  [   12/  240]\n",
+      "loss: 1.056207  [  132/  240]\n",
+      "Epoch 73\n",
+      "-------------------------------\n",
+      "loss: 0.993322  [   12/  240]\n",
+      "loss: 1.077630  [  132/  240]\n",
+      "Epoch 74\n",
+      "-------------------------------\n",
+      "loss: 0.956259  [   12/  240]\n",
+      "loss: 1.018286  [  132/  240]\n",
+      "Epoch 75\n",
+      "-------------------------------\n",
+      "loss: 0.918359  [   12/  240]\n",
+      "loss: 0.884498  [  132/  240]\n",
+      "Epoch 76\n",
+      "-------------------------------\n",
+      "loss: 0.867537  [   12/  240]\n",
+      "loss: 0.872493  [  132/  240]\n",
+      "Epoch 77\n",
+      "-------------------------------\n",
+      "loss: 24.992321  [   12/  240]\n",
+      "loss: 0.826090  [  132/  240]\n",
+      "Epoch 78\n",
+      "-------------------------------\n",
+      "loss: 0.867809  [   12/  240]\n",
+      "loss: 1.058265  [  132/  240]\n",
+      "Epoch 79\n",
+      "-------------------------------\n",
+      "loss: 0.870663  [   12/  240]\n",
+      "loss: 0.918959  [  132/  240]\n",
+      "Epoch 80\n",
+      "-------------------------------\n",
+      "loss: 0.826538  [   12/  240]\n",
+      "loss: 0.797239  [  132/  240]\n",
+      "Epoch 81\n",
+      "-------------------------------\n",
+      "loss: 0.852126  [   12/  240]\n",
+      "loss: 0.948225  [  132/  240]\n",
+      "Epoch 82\n",
+      "-------------------------------\n",
+      "loss: 0.854743  [   12/  240]\n",
+      "loss: 0.817912  [  132/  240]\n",
+      "Epoch 83\n",
+      "-------------------------------\n",
+      "loss: 0.975670  [   12/  240]\n",
+      "loss: 0.815692  [  132/  240]\n",
+      "Epoch 84\n",
+      "-------------------------------\n",
+      "loss: 0.833513  [   12/  240]\n",
+      "loss: 0.797355  [  132/  240]\n",
+      "Epoch 85\n",
+      "-------------------------------\n",
+      "loss: 0.778383  [   12/  240]\n",
+      "loss: 0.821114  [  132/  240]\n",
+      "Epoch 86\n",
+      "-------------------------------\n",
+      "loss: 0.804280  [   12/  240]\n",
+      "loss: 0.773760  [  132/  240]\n",
+      "Epoch 87\n",
+      "-------------------------------\n",
+      "loss: 26.835640  [   12/  240]\n",
+      "loss: 0.863283  [  132/  240]\n",
+      "Epoch 88\n",
+      "-------------------------------\n",
+      "loss: 0.804913  [   12/  240]\n",
+      "loss: 0.768397  [  132/  240]\n",
+      "Epoch 89\n",
+      "-------------------------------\n",
+      "loss: 26.520769  [   12/  240]\n",
+      "loss: 19.110596  [  132/  240]\n",
+      "Epoch 90\n",
+      "-------------------------------\n",
+      "loss: 0.775290  [   12/  240]\n",
+      "loss: 0.777933  [  132/  240]\n",
+      "Epoch 91\n",
+      "-------------------------------\n",
+      "loss: 0.866682  [   12/  240]\n",
+      "loss: 0.803626  [  132/  240]\n",
+      "Epoch 92\n",
+      "-------------------------------\n",
+      "loss: 0.796024  [   12/  240]\n",
+      "loss: 0.793135  [  132/  240]\n",
+      "Epoch 93\n",
+      "-------------------------------\n",
+      "loss: 0.792549  [   12/  240]\n",
+      "loss: 0.790713  [  132/  240]\n",
+      "Epoch 94\n",
+      "-------------------------------\n",
+      "loss: 0.764315  [   12/  240]\n",
+      "loss: 0.881296  [  132/  240]\n",
+      "Epoch 95\n",
+      "-------------------------------\n",
+      "loss: 0.794830  [   12/  240]\n",
+      "loss: 0.846523  [  132/  240]\n",
+      "Epoch 96\n",
+      "-------------------------------\n",
+      "loss: 19.105461  [   12/  240]\n",
+      "loss: 0.799195  [  132/  240]\n",
+      "Epoch 97\n",
+      "-------------------------------\n",
+      "loss: 0.855702  [   12/  240]\n",
+      "loss: 0.822797  [  132/  240]\n",
+      "Epoch 98\n",
+      "-------------------------------\n",
+      "loss: 19.112574  [   12/  240]\n",
+      "loss: 0.755735  [  132/  240]\n",
+      "Epoch 99\n",
+      "-------------------------------\n",
+      "loss: 0.829628  [   12/  240]\n",
+      "loss: 26.331259  [  132/  240]\n",
+      "Epoch 100\n",
+      "-------------------------------\n",
+      "loss: 0.762956  [   12/  240]\n",
+      "loss: 0.747768  [  132/  240]\n",
+      "Epoch 101\n",
+      "-------------------------------\n",
+      "loss: 0.819976  [   12/  240]\n",
+      "loss: 26.050211  [  132/  240]\n",
+      "Epoch 102\n",
+      "-------------------------------\n",
+      "loss: 24.911037  [   12/  240]\n",
+      "loss: 0.788500  [  132/  240]\n",
+      "Epoch 103\n",
+      "-------------------------------\n",
+      "loss: 0.762518  [   12/  240]\n",
+      "loss: 0.754093  [  132/  240]\n",
+      "Epoch 104\n",
+      "-------------------------------\n",
+      "loss: 0.757778  [   12/  240]\n",
+      "loss: 25.483772  [  132/  240]\n",
+      "Epoch 105\n",
+      "-------------------------------\n",
+      "loss: 0.774396  [   12/  240]\n",
+      "loss: 24.924021  [  132/  240]\n",
+      "Epoch 106\n",
+      "-------------------------------\n",
+      "loss: 0.860951  [   12/  240]\n",
+      "loss: 25.646545  [  132/  240]\n",
+      "Epoch 107\n",
+      "-------------------------------\n",
+      "loss: 0.762656  [   12/  240]\n",
+      "loss: 0.773041  [  132/  240]\n",
+      "Epoch 108\n",
+      "-------------------------------\n",
+      "loss: 0.827246  [   12/  240]\n",
+      "loss: 0.799193  [  132/  240]\n",
+      "Epoch 109\n",
+      "-------------------------------\n",
+      "loss: 0.755089  [   12/  240]\n",
+      "loss: 0.768211  [  132/  240]\n",
+      "Epoch 110\n",
+      "-------------------------------\n",
+      "loss: 0.750724  [   12/  240]\n",
+      "loss: 0.754573  [  132/  240]\n",
+      "Epoch 111\n",
+      "-------------------------------\n",
+      "loss: 0.770611  [   12/  240]\n",
+      "loss: 0.832263  [  132/  240]\n",
+      "Epoch 112\n",
+      "-------------------------------\n",
+      "loss: 0.783227  [   12/  240]\n",
+      "loss: 0.752168  [  132/  240]\n",
+      "Epoch 113\n",
+      "-------------------------------\n",
+      "loss: 0.796339  [   12/  240]\n",
+      "loss: 0.761609  [  132/  240]\n",
+      "Epoch 114\n",
+      "-------------------------------\n",
+      "loss: 0.755191  [   12/  240]\n",
+      "loss: 0.812314  [  132/  240]\n",
+      "Epoch 115\n",
+      "-------------------------------\n",
+      "loss: 0.781752  [   12/  240]\n",
+      "loss: 0.756835  [  132/  240]\n",
+      "Epoch 116\n",
+      "-------------------------------\n",
+      "loss: 0.825940  [   12/  240]\n",
+      "loss: 0.760349  [  132/  240]\n",
+      "Epoch 117\n",
+      "-------------------------------\n",
+      "loss: 0.737685  [   12/  240]\n",
+      "loss: 0.758577  [  132/  240]\n",
+      "Epoch 118\n",
+      "-------------------------------\n",
+      "loss: 0.826932  [   12/  240]\n",
+      "loss: 0.755998  [  132/  240]\n",
+      "Epoch 119\n",
+      "-------------------------------\n",
+      "loss: 0.799743  [   12/  240]\n",
+      "loss: 24.183624  [  132/  240]\n",
+      "Epoch 120\n",
+      "-------------------------------\n",
+      "loss: 0.748408  [   12/  240]\n",
+      "loss: 0.758820  [  132/  240]\n",
+      "Epoch 121\n",
+      "-------------------------------\n",
+      "loss: 0.811288  [   12/  240]\n",
+      "loss: 0.761295  [  132/  240]\n",
+      "Epoch 122\n",
+      "-------------------------------\n",
+      "loss: 0.778416  [   12/  240]\n",
+      "loss: 0.756212  [  132/  240]\n",
+      "Epoch 123\n",
+      "-------------------------------\n",
+      "loss: 0.742463  [   12/  240]\n",
+      "loss: 0.740075  [  132/  240]\n",
+      "Epoch 124\n",
+      "-------------------------------\n",
+      "loss: 0.770352  [   12/  240]\n",
+      "loss: 0.779086  [  132/  240]\n",
+      "Epoch 125\n",
+      "-------------------------------\n",
+      "loss: 0.762029  [   12/  240]\n",
+      "loss: 0.769137  [  132/  240]\n",
+      "Epoch 126\n",
+      "-------------------------------\n",
+      "loss: 0.770764  [   12/  240]\n",
+      "loss: 0.716541  [  132/  240]\n",
+      "Epoch 127\n",
+      "-------------------------------\n",
+      "loss: 0.774299  [   12/  240]\n",
+      "loss: 23.713043  [  132/  240]\n",
+      "Epoch 128\n",
+      "-------------------------------\n",
+      "loss: 0.765517  [   12/  240]\n",
+      "loss: 0.757670  [  132/  240]\n",
+      "Epoch 129\n",
+      "-------------------------------\n",
+      "loss: 0.781306  [   12/  240]\n",
+      "loss: 0.737803  [  132/  240]\n",
+      "Epoch 130\n",
+      "-------------------------------\n",
+      "loss: 0.766404  [   12/  240]\n",
+      "loss: 0.743729  [  132/  240]\n",
+      "Epoch 131\n",
+      "-------------------------------\n",
+      "loss: 0.747539  [   12/  240]\n",
+      "loss: 0.863318  [  132/  240]\n",
+      "Epoch 132\n",
+      "-------------------------------\n",
+      "loss: 24.899851  [   12/  240]\n",
+      "loss: 0.822364  [  132/  240]\n",
+      "Epoch 133\n",
+      "-------------------------------\n",
+      "loss: 0.743308  [   12/  240]\n",
+      "loss: 0.738269  [  132/  240]\n",
+      "Epoch 134\n",
+      "-------------------------------\n",
+      "loss: 0.855956  [   12/  240]\n",
+      "loss: 0.752757  [  132/  240]\n",
+      "Epoch 135\n",
+      "-------------------------------\n",
+      "loss: 0.761731  [   12/  240]\n",
+      "loss: 0.740654  [  132/  240]\n",
+      "Epoch 136\n",
+      "-------------------------------\n",
+      "loss: 19.109028  [   12/  240]\n",
+      "loss: 0.783224  [  132/  240]\n",
+      "Epoch 137\n",
+      "-------------------------------\n",
+      "loss: 0.770941  [   12/  240]\n",
+      "loss: 0.717841  [  132/  240]\n",
+      "Epoch 138\n",
+      "-------------------------------\n",
+      "loss: 0.743567  [   12/  240]\n",
+      "loss: 0.768516  [  132/  240]\n",
+      "Epoch 139\n",
+      "-------------------------------\n",
+      "loss: 0.752703  [   12/  240]\n",
+      "loss: 0.729162  [  132/  240]\n",
+      "Epoch 140\n",
+      "-------------------------------\n",
+      "loss: 0.799042  [   12/  240]\n",
+      "loss: 0.761270  [  132/  240]\n",
+      "Epoch 141\n",
+      "-------------------------------\n",
+      "loss: 0.724938  [   12/  240]\n",
+      "loss: 0.722089  [  132/  240]\n",
+      "Epoch 142\n",
+      "-------------------------------\n",
+      "loss: 0.763858  [   12/  240]\n",
+      "loss: 0.742919  [  132/  240]\n",
+      "Epoch 143\n",
+      "-------------------------------\n",
+      "loss: 0.798563  [   12/  240]\n",
+      "loss: 0.760687  [  132/  240]\n",
+      "Epoch 144\n",
+      "-------------------------------\n",
+      "loss: 0.805421  [   12/  240]\n",
+      "loss: 0.778282  [  132/  240]\n",
+      "Epoch 145\n",
+      "-------------------------------\n",
+      "loss: 0.748229  [   12/  240]\n",
+      "loss: 0.730958  [  132/  240]\n",
+      "Epoch 146\n",
+      "-------------------------------\n",
+      "loss: 0.743372  [   12/  240]\n",
+      "loss: 0.737600  [  132/  240]\n",
+      "Epoch 147\n",
+      "-------------------------------\n",
+      "loss: 0.737913  [   12/  240]\n",
+      "loss: 0.706045  [  132/  240]\n",
+      "Epoch 148\n",
+      "-------------------------------\n",
+      "loss: 0.748985  [   12/  240]\n",
+      "loss: 0.788640  [  132/  240]\n",
+      "Epoch 149\n",
+      "-------------------------------\n",
+      "loss: 0.781389  [   12/  240]\n",
+      "loss: 0.843032  [  132/  240]\n",
+      "Epoch 150\n",
+      "-------------------------------\n",
+      "loss: 0.757440  [   12/  240]\n",
+      "loss: 21.631023  [  132/  240]\n",
+      "Epoch 151\n",
+      "-------------------------------\n",
+      "loss: 0.738275  [   12/  240]\n",
+      "loss: 0.749734  [  132/  240]\n",
+      "Epoch 152\n",
+      "-------------------------------\n",
+      "loss: 0.751316  [   12/  240]\n",
+      "loss: 21.392103  [  132/  240]\n",
+      "Epoch 153\n",
+      "-------------------------------\n",
+      "loss: 21.266928  [   12/  240]\n",
+      "loss: 0.755438  [  132/  240]\n",
+      "Epoch 154\n",
+      "-------------------------------\n",
+      "loss: 0.741461  [   12/  240]\n",
+      "loss: 0.712775  [  132/  240]\n",
+      "Epoch 155\n",
+      "-------------------------------\n",
+      "loss: 0.760358  [   12/  240]\n",
+      "loss: 0.701295  [  132/  240]\n",
+      "Epoch 156\n",
+      "-------------------------------\n",
+      "loss: 0.720837  [   12/  240]\n",
+      "loss: 0.753206  [  132/  240]\n",
+      "Epoch 157\n",
+      "-------------------------------\n",
+      "loss: 0.737418  [   12/  240]\n",
+      "loss: 0.740694  [  132/  240]\n",
+      "Epoch 158\n",
+      "-------------------------------\n",
+      "loss: 0.714467  [   12/  240]\n",
+      "loss: 0.713096  [  132/  240]\n",
+      "Epoch 159\n",
+      "-------------------------------\n",
+      "loss: 0.769238  [   12/  240]\n",
+      "loss: 24.873816  [  132/  240]\n",
+      "Epoch 160\n",
+      "-------------------------------\n",
+      "loss: 0.730489  [   12/  240]\n",
+      "loss: 0.755486  [  132/  240]\n",
+      "Epoch 161\n",
+      "-------------------------------\n",
+      "loss: 20.915621  [   12/  240]\n",
+      "loss: 0.765852  [  132/  240]\n",
+      "Epoch 162\n",
+      "-------------------------------\n",
+      "loss: 0.733610  [   12/  240]\n",
+      "loss: 0.793433  [  132/  240]\n",
+      "Epoch 163\n",
+      "-------------------------------\n",
+      "loss: 0.744526  [   12/  240]\n",
+      "loss: 0.708314  [  132/  240]\n",
+      "Epoch 164\n",
+      "-------------------------------\n",
+      "loss: 24.888470  [   12/  240]\n",
+      "loss: 0.764530  [  132/  240]\n",
+      "Epoch 165\n",
+      "-------------------------------\n",
+      "loss: 0.752846  [   12/  240]\n",
+      "loss: 0.769699  [  132/  240]\n",
+      "Epoch 166\n",
+      "-------------------------------\n",
+      "loss: 0.738446  [   12/  240]\n",
+      "loss: 0.705085  [  132/  240]\n",
+      "Epoch 167\n",
+      "-------------------------------\n",
+      "loss: 0.805467  [   12/  240]\n",
+      "loss: 20.761816  [  132/  240]\n",
+      "Epoch 168\n",
+      "-------------------------------\n",
+      "loss: 0.745640  [   12/  240]\n",
+      "loss: 0.750508  [  132/  240]\n",
+      "Epoch 169\n",
+      "-------------------------------\n",
+      "loss: 0.731987  [   12/  240]\n",
+      "loss: 0.694678  [  132/  240]\n",
+      "Epoch 170\n",
+      "-------------------------------\n",
+      "loss: 24.884092  [   12/  240]\n",
+      "loss: 19.062004  [  132/  240]\n",
+      "Epoch 171\n",
+      "-------------------------------\n",
+      "loss: 20.408443  [   12/  240]\n",
+      "loss: 0.804953  [  132/  240]\n",
+      "Epoch 172\n",
+      "-------------------------------\n",
+      "loss: 0.746346  [   12/  240]\n",
+      "loss: 0.765819  [  132/  240]\n",
+      "Epoch 173\n",
+      "-------------------------------\n",
+      "loss: 0.724415  [   12/  240]\n",
+      "loss: 24.844360  [  132/  240]\n",
+      "Epoch 174\n",
+      "-------------------------------\n",
+      "loss: 0.713483  [   12/  240]\n",
+      "loss: 0.790998  [  132/  240]\n",
+      "Epoch 175\n",
+      "-------------------------------\n",
+      "loss: 0.727793  [   12/  240]\n",
+      "loss: 0.704429  [  132/  240]\n",
+      "Epoch 176\n",
+      "-------------------------------\n",
+      "loss: 0.733582  [   12/  240]\n",
+      "loss: 0.736067  [  132/  240]\n",
+      "Epoch 177\n",
+      "-------------------------------\n",
+      "loss: 0.760796  [   12/  240]\n",
+      "loss: 0.726366  [  132/  240]\n",
+      "Epoch 178\n",
+      "-------------------------------\n",
+      "loss: 0.741501  [   12/  240]\n",
+      "loss: 20.167385  [  132/  240]\n",
+      "Epoch 179\n",
+      "-------------------------------\n",
+      "loss: 0.756091  [   12/  240]\n",
+      "loss: 0.722886  [  132/  240]\n",
+      "Epoch 180\n",
+      "-------------------------------\n",
+      "loss: 37.826012  [   12/  240]\n",
+      "loss: 0.716488  [  132/  240]\n",
+      "Epoch 181\n",
+      "-------------------------------\n",
+      "loss: 0.742107  [   12/  240]\n",
+      "loss: 0.787945  [  132/  240]\n",
+      "Epoch 182\n",
+      "-------------------------------\n",
+      "loss: 0.764757  [   12/  240]\n",
+      "loss: 0.748063  [  132/  240]\n",
+      "Epoch 183\n",
+      "-------------------------------\n",
+      "loss: 0.765754  [   12/  240]\n",
+      "loss: 0.751583  [  132/  240]\n",
+      "Epoch 184\n",
+      "-------------------------------\n",
+      "loss: 0.746295  [   12/  240]\n",
+      "loss: 0.748388  [  132/  240]\n",
+      "Epoch 185\n",
+      "-------------------------------\n",
+      "loss: 0.742548  [   12/  240]\n",
+      "loss: 0.731993  [  132/  240]\n",
+      "Epoch 186\n",
+      "-------------------------------\n",
+      "loss: 0.711927  [   12/  240]\n",
+      "loss: 0.786947  [  132/  240]\n",
+      "Epoch 187\n",
+      "-------------------------------\n",
+      "loss: 0.711539  [   12/  240]\n",
+      "loss: 0.719651  [  132/  240]\n",
+      "Epoch 188\n",
+      "-------------------------------\n",
+      "loss: 0.743296  [   12/  240]\n",
+      "loss: 0.759052  [  132/  240]\n",
+      "Epoch 189\n",
+      "-------------------------------\n",
+      "loss: 0.747429  [   12/  240]\n",
+      "loss: 0.741085  [  132/  240]\n",
+      "Epoch 190\n",
+      "-------------------------------\n",
+      "loss: 0.755498  [   12/  240]\n",
+      "loss: 0.699223  [  132/  240]\n",
+      "Epoch 191\n",
+      "-------------------------------\n",
+      "loss: 19.085520  [   12/  240]\n",
+      "loss: 0.748960  [  132/  240]\n",
+      "Epoch 192\n",
+      "-------------------------------\n",
+      "loss: 19.354300  [   12/  240]\n",
+      "loss: 0.738580  [  132/  240]\n",
+      "Epoch 193\n",
+      "-------------------------------\n",
+      "loss: 0.750013  [   12/  240]\n",
+      "loss: 0.806999  [  132/  240]\n",
+      "Epoch 194\n",
+      "-------------------------------\n",
+      "loss: 0.728495  [   12/  240]\n",
+      "loss: 0.702252  [  132/  240]\n",
+      "Epoch 195\n",
+      "-------------------------------\n",
+      "loss: 0.749485  [   12/  240]\n",
+      "loss: 0.733194  [  132/  240]\n",
+      "Epoch 196\n",
+      "-------------------------------\n",
+      "loss: 0.740722  [   12/  240]\n",
+      "loss: 0.731696  [  132/  240]\n",
+      "Epoch 197\n",
+      "-------------------------------\n",
+      "loss: 0.737052  [   12/  240]\n",
+      "loss: 0.722875  [  132/  240]\n",
+      "Epoch 198\n",
+      "-------------------------------\n",
+      "loss: 0.745713  [   12/  240]\n",
+      "loss: 0.721238  [  132/  240]\n",
+      "Epoch 199\n",
+      "-------------------------------\n",
+      "loss: 18.685354  [   12/  240]\n",
+      "loss: 0.743099  [  132/  240]\n",
+      "Epoch 200\n",
+      "-------------------------------\n",
+      "loss: 0.741237  [   12/  240]\n",
+      "loss: 0.772574  [  132/  240]\n",
+      "Epoch 201\n",
+      "-------------------------------\n",
+      "loss: 0.715898  [   12/  240]\n",
+      "loss: 0.714672  [  132/  240]\n",
+      "Epoch 202\n",
+      "-------------------------------\n",
+      "loss: 0.751376  [   12/  240]\n",
+      "loss: 0.713908  [  132/  240]\n",
+      "Epoch 203\n",
+      "-------------------------------\n",
+      "loss: 18.579397  [   12/  240]\n",
+      "loss: 0.715354  [  132/  240]\n",
+      "Epoch 204\n",
+      "-------------------------------\n",
+      "loss: 0.717241  [   12/  240]\n",
+      "loss: 0.764435  [  132/  240]\n",
+      "Epoch 205\n",
+      "-------------------------------\n",
+      "loss: 0.723228  [   12/  240]\n",
+      "loss: 0.733689  [  132/  240]\n",
+      "Epoch 206\n",
+      "-------------------------------\n",
+      "loss: 0.744204  [   12/  240]\n",
+      "loss: 0.699542  [  132/  240]\n",
+      "Epoch 207\n",
+      "-------------------------------\n",
+      "loss: 0.770918  [   12/  240]\n",
+      "loss: 0.725624  [  132/  240]\n",
+      "Epoch 208\n",
+      "-------------------------------\n",
+      "loss: 0.820731  [   12/  240]\n",
+      "loss: 0.689943  [  132/  240]\n",
+      "Epoch 209\n",
+      "-------------------------------\n",
+      "loss: 18.157301  [   12/  240]\n",
+      "loss: 0.748126  [  132/  240]\n",
+      "Epoch 210\n",
+      "-------------------------------\n",
+      "loss: 0.745101  [   12/  240]\n",
+      "loss: 0.826853  [  132/  240]\n",
+      "Epoch 211\n",
+      "-------------------------------\n",
+      "loss: 0.752092  [   12/  240]\n",
+      "loss: 0.746889  [  132/  240]\n",
+      "Epoch 212\n",
+      "-------------------------------\n",
+      "loss: 24.854631  [   12/  240]\n",
+      "loss: 0.759155  [  132/  240]\n",
+      "Epoch 213\n",
+      "-------------------------------\n",
+      "loss: 0.706125  [   12/  240]\n",
+      "loss: 0.717615  [  132/  240]\n",
+      "Epoch 214\n",
+      "-------------------------------\n",
+      "loss: 0.763068  [   12/  240]\n",
+      "loss: 0.726547  [  132/  240]\n",
+      "Epoch 215\n",
+      "-------------------------------\n",
+      "loss: 0.711609  [   12/  240]\n",
+      "loss: 0.799008  [  132/  240]\n",
+      "Epoch 216\n",
+      "-------------------------------\n",
+      "loss: 0.686945  [   12/  240]\n",
+      "loss: 0.775805  [  132/  240]\n",
+      "Epoch 217\n",
+      "-------------------------------\n",
+      "loss: 0.741048  [   12/  240]\n",
+      "loss: 0.688331  [  132/  240]\n",
+      "Epoch 218\n",
+      "-------------------------------\n",
+      "loss: 0.723453  [   12/  240]\n",
+      "loss: 0.726240  [  132/  240]\n",
+      "Epoch 219\n",
+      "-------------------------------\n",
+      "loss: 0.744929  [   12/  240]\n",
+      "loss: 0.705450  [  132/  240]\n",
+      "Epoch 220\n",
+      "-------------------------------\n",
+      "loss: 0.723632  [   12/  240]\n",
+      "loss: 0.710132  [  132/  240]\n",
+      "Epoch 221\n",
+      "-------------------------------\n",
+      "loss: 0.685570  [   12/  240]\n",
+      "loss: 0.727209  [  132/  240]\n",
+      "Epoch 222\n",
+      "-------------------------------\n",
+      "loss: 0.711545  [   12/  240]\n",
+      "loss: 19.061712  [  132/  240]\n",
+      "Epoch 223\n",
+      "-------------------------------\n",
+      "loss: 0.717574  [   12/  240]\n",
+      "loss: 0.702137  [  132/  240]\n",
+      "Epoch 224\n",
+      "-------------------------------\n",
+      "loss: 0.701294  [   12/  240]\n",
+      "loss: 0.679335  [  132/  240]\n",
+      "Epoch 225\n",
+      "-------------------------------\n",
+      "loss: 19.059959  [   12/  240]\n",
+      "loss: 0.734564  [  132/  240]\n",
+      "Epoch 226\n",
+      "-------------------------------\n",
+      "loss: 0.727230  [   12/  240]\n",
+      "loss: 0.776725  [  132/  240]\n",
+      "Epoch 227\n",
+      "-------------------------------\n",
+      "loss: 0.709015  [   12/  240]\n",
+      "loss: 0.704756  [  132/  240]\n",
+      "Epoch 228\n",
+      "-------------------------------\n",
+      "loss: 0.730201  [   12/  240]\n",
+      "loss: 0.692140  [  132/  240]\n",
+      "Epoch 229\n",
+      "-------------------------------\n",
+      "loss: 0.707709  [   12/  240]\n",
+      "loss: 0.728010  [  132/  240]\n",
+      "Epoch 230\n",
+      "-------------------------------\n",
+      "loss: 0.685025  [   12/  240]\n",
+      "loss: 0.693796  [  132/  240]\n",
+      "Epoch 231\n",
+      "-------------------------------\n",
+      "loss: 0.687448  [   12/  240]\n",
+      "loss: 0.726771  [  132/  240]\n",
+      "Epoch 232\n",
+      "-------------------------------\n",
+      "loss: 24.867670  [   12/  240]\n",
+      "loss: 0.671139  [  132/  240]\n",
+      "Epoch 233\n",
+      "-------------------------------\n",
+      "loss: 0.672847  [   12/  240]\n",
+      "loss: 0.716054  [  132/  240]\n",
+      "Epoch 234\n",
+      "-------------------------------\n",
+      "loss: 0.695528  [   12/  240]\n",
+      "loss: 0.729434  [  132/  240]\n",
+      "Epoch 235\n",
+      "-------------------------------\n",
+      "loss: 0.707604  [   12/  240]\n",
+      "loss: 0.706395  [  132/  240]\n",
+      "Epoch 236\n",
+      "-------------------------------\n",
+      "loss: 0.686879  [   12/  240]\n",
+      "loss: 0.724360  [  132/  240]\n",
+      "Epoch 237\n",
+      "-------------------------------\n",
+      "loss: 0.709373  [   12/  240]\n",
+      "loss: 0.741107  [  132/  240]\n",
+      "Epoch 238\n",
+      "-------------------------------\n",
+      "loss: 24.855419  [   12/  240]\n",
+      "loss: 0.714944  [  132/  240]\n",
+      "Epoch 239\n",
+      "-------------------------------\n",
+      "loss: 0.686095  [   12/  240]\n",
+      "loss: 0.703539  [  132/  240]\n",
+      "Epoch 240\n",
+      "-------------------------------\n",
+      "loss: 0.671129  [   12/  240]\n",
+      "loss: 0.733130  [  132/  240]\n",
+      "Epoch 241\n",
+      "-------------------------------\n",
+      "loss: 0.723860  [   12/  240]\n",
+      "loss: 15.507588  [  132/  240]\n",
+      "Epoch 242\n",
+      "-------------------------------\n",
+      "loss: 0.666405  [   12/  240]\n",
+      "loss: 0.666636  [  132/  240]\n",
+      "Epoch 243\n",
+      "-------------------------------\n",
+      "loss: 0.668706  [   12/  240]\n",
+      "loss: 0.675140  [  132/  240]\n",
+      "Epoch 244\n",
+      "-------------------------------\n",
+      "loss: 24.785295  [   12/  240]\n",
+      "loss: 0.668677  [  132/  240]\n",
+      "Epoch 245\n",
+      "-------------------------------\n",
+      "loss: 0.690136  [   12/  240]\n",
+      "loss: 0.700700  [  132/  240]\n",
+      "Epoch 246\n",
+      "-------------------------------\n",
+      "loss: 0.699744  [   12/  240]\n",
+      "loss: 0.695882  [  132/  240]\n",
+      "Epoch 247\n",
+      "-------------------------------\n",
+      "loss: 0.682396  [   12/  240]\n",
+      "loss: 0.650242  [  132/  240]\n",
+      "Epoch 248\n",
+      "-------------------------------\n",
+      "loss: 0.708496  [   12/  240]\n",
+      "loss: 0.651965  [  132/  240]\n",
+      "Epoch 249\n",
+      "-------------------------------\n",
+      "loss: 0.686695  [   12/  240]\n",
+      "loss: 0.642502  [  132/  240]\n",
+      "Epoch 250\n",
+      "-------------------------------\n",
+      "loss: 0.658240  [   12/  240]\n",
+      "loss: 0.677959  [  132/  240]\n",
+      "Epoch 251\n",
+      "-------------------------------\n",
+      "loss: 0.688773  [   12/  240]\n",
+      "loss: 0.679020  [  132/  240]\n",
+      "Epoch 252\n",
+      "-------------------------------\n",
+      "loss: 0.677303  [   12/  240]\n",
+      "loss: 0.665342  [  132/  240]\n",
+      "Epoch 253\n",
+      "-------------------------------\n",
+      "loss: 0.691356  [   12/  240]\n",
+      "loss: 0.655760  [  132/  240]\n",
+      "Epoch 254\n",
+      "-------------------------------\n",
+      "loss: 0.663553  [   12/  240]\n",
+      "loss: 0.656745  [  132/  240]\n",
+      "Epoch 255\n",
+      "-------------------------------\n",
+      "loss: 0.679018  [   12/  240]\n",
+      "loss: 0.650076  [  132/  240]\n",
+      "Epoch 256\n",
+      "-------------------------------\n",
+      "loss: 0.672170  [   12/  240]\n",
+      "loss: 0.673465  [  132/  240]\n",
+      "Epoch 257\n",
+      "-------------------------------\n",
+      "loss: 0.654162  [   12/  240]\n",
+      "loss: 0.673636  [  132/  240]\n",
+      "Epoch 258\n",
+      "-------------------------------\n",
+      "loss: 0.644282  [   12/  240]\n",
+      "loss: 0.690309  [  132/  240]\n",
+      "Epoch 259\n",
+      "-------------------------------\n",
+      "loss: 0.655806  [   12/  240]\n",
+      "loss: 0.669881  [  132/  240]\n",
+      "Epoch 260\n",
+      "-------------------------------\n",
+      "loss: 0.648449  [   12/  240]\n",
+      "loss: 0.630259  [  132/  240]\n",
+      "Epoch 261\n",
+      "-------------------------------\n",
+      "loss: 0.665260  [   12/  240]\n",
+      "loss: 0.639449  [  132/  240]\n",
+      "Epoch 262\n",
+      "-------------------------------\n",
+      "loss: 0.643118  [   12/  240]\n",
+      "loss: 0.665032  [  132/  240]\n",
+      "Epoch 263\n",
+      "-------------------------------\n",
+      "loss: 0.616057  [   12/  240]\n",
+      "loss: 24.767809  [  132/  240]\n",
+      "Epoch 264\n",
+      "-------------------------------\n",
+      "loss: 0.632257  [   12/  240]\n",
+      "loss: 0.654202  [  132/  240]\n",
+      "Epoch 265\n",
+      "-------------------------------\n",
+      "loss: 0.660660  [   12/  240]\n",
+      "loss: 0.669866  [  132/  240]\n",
+      "Epoch 266\n",
+      "-------------------------------\n",
+      "loss: 0.643258  [   12/  240]\n",
+      "loss: 0.623167  [  132/  240]\n",
+      "Epoch 267\n",
+      "-------------------------------\n",
+      "loss: 0.639379  [   12/  240]\n",
+      "loss: 11.764853  [  132/  240]\n",
+      "Epoch 268\n",
+      "-------------------------------\n",
+      "loss: 0.623519  [   12/  240]\n",
+      "loss: 0.646703  [  132/  240]\n",
+      "Epoch 269\n",
+      "-------------------------------\n",
+      "loss: 0.620854  [   12/  240]\n",
+      "loss: 0.663550  [  132/  240]\n",
+      "Epoch 270\n",
+      "-------------------------------\n",
+      "loss: 0.626448  [   12/  240]\n",
+      "loss: 0.651102  [  132/  240]\n",
+      "Epoch 271\n",
+      "-------------------------------\n",
+      "loss: 0.639866  [   12/  240]\n",
+      "loss: 0.614343  [  132/  240]\n",
+      "Epoch 272\n",
+      "-------------------------------\n",
+      "loss: 0.631630  [   12/  240]\n",
+      "loss: 0.610753  [  132/  240]\n",
+      "Epoch 273\n",
+      "-------------------------------\n",
+      "loss: 0.607945  [   12/  240]\n",
+      "loss: 0.615416  [  132/  240]\n",
+      "Epoch 274\n",
+      "-------------------------------\n",
+      "loss: 0.623100  [   12/  240]\n",
+      "loss: 24.783854  [  132/  240]\n",
+      "Epoch 275\n",
+      "-------------------------------\n",
+      "loss: 0.666848  [   12/  240]\n",
+      "loss: 0.620967  [  132/  240]\n",
+      "Epoch 276\n",
+      "-------------------------------\n",
+      "loss: 0.640230  [   12/  240]\n",
+      "loss: 0.614040  [  132/  240]\n",
+      "Epoch 277\n",
+      "-------------------------------\n",
+      "loss: 10.153914  [   12/  240]\n",
+      "loss: 0.659580  [  132/  240]\n",
+      "Epoch 278\n",
+      "-------------------------------\n",
+      "loss: 0.626281  [   12/  240]\n",
+      "loss: 0.616286  [  132/  240]\n",
+      "Epoch 279\n",
+      "-------------------------------\n",
+      "loss: 24.743822  [   12/  240]\n",
+      "loss: 0.658540  [  132/  240]\n",
+      "Epoch 280\n",
+      "-------------------------------\n",
+      "loss: 0.626426  [   12/  240]\n",
+      "loss: 0.616303  [  132/  240]\n",
+      "Epoch 281\n",
+      "-------------------------------\n",
+      "loss: 0.641541  [   12/  240]\n",
+      "loss: 0.631519  [  132/  240]\n",
+      "Epoch 282\n",
+      "-------------------------------\n",
+      "loss: 0.613703  [   12/  240]\n",
+      "loss: 0.627136  [  132/  240]\n",
+      "Epoch 283\n",
+      "-------------------------------\n",
+      "loss: 0.620009  [   12/  240]\n",
+      "loss: 0.603383  [  132/  240]\n",
+      "Epoch 284\n",
+      "-------------------------------\n",
+      "loss: 0.621318  [   12/  240]\n",
+      "loss: 0.629973  [  132/  240]\n",
+      "Epoch 285\n",
+      "-------------------------------\n",
+      "loss: 0.641299  [   12/  240]\n",
+      "loss: 0.630579  [  132/  240]\n",
+      "Epoch 286\n",
+      "-------------------------------\n",
+      "loss: 0.622224  [   12/  240]\n",
+      "loss: 0.605684  [  132/  240]\n",
+      "Epoch 287\n",
+      "-------------------------------\n",
+      "loss: 0.628760  [   12/  240]\n",
+      "loss: 0.627962  [  132/  240]\n",
+      "Epoch 288\n",
+      "-------------------------------\n",
+      "loss: 0.636673  [   12/  240]\n",
+      "loss: 24.752956  [  132/  240]\n",
+      "Epoch 289\n",
+      "-------------------------------\n",
+      "loss: 0.617128  [   12/  240]\n",
+      "loss: 0.627085  [  132/  240]\n",
+      "Epoch 290\n",
+      "-------------------------------\n",
+      "loss: 0.603013  [   12/  240]\n",
+      "loss: 0.624260  [  132/  240]\n",
+      "Epoch 291\n",
+      "-------------------------------\n",
+      "loss: 0.613730  [   12/  240]\n",
+      "loss: 0.626347  [  132/  240]\n",
+      "Epoch 292\n",
+      "-------------------------------\n",
+      "loss: 0.622470  [   12/  240]\n",
+      "loss: 0.642590  [  132/  240]\n",
+      "Epoch 293\n",
+      "-------------------------------\n",
+      "loss: 0.601692  [   12/  240]\n",
+      "loss: 0.647440  [  132/  240]\n",
+      "Epoch 294\n",
+      "-------------------------------\n",
+      "loss: 0.625025  [   12/  240]\n",
+      "loss: 0.631902  [  132/  240]\n",
+      "Epoch 295\n",
+      "-------------------------------\n",
+      "loss: 0.628118  [   12/  240]\n",
+      "loss: 0.647914  [  132/  240]\n",
+      "Epoch 296\n",
+      "-------------------------------\n",
+      "loss: 18.944721  [   12/  240]\n",
+      "loss: 0.619774  [  132/  240]\n",
+      "Epoch 297\n",
+      "-------------------------------\n",
+      "loss: 0.614956  [   12/  240]\n",
+      "loss: 0.634540  [  132/  240]\n",
+      "Epoch 298\n",
+      "-------------------------------\n",
+      "loss: 0.607087  [   12/  240]\n",
+      "loss: 0.619027  [  132/  240]\n",
+      "Epoch 299\n",
+      "-------------------------------\n",
+      "loss: 0.603270  [   12/  240]\n",
+      "loss: 24.747189  [  132/  240]\n",
+      "Epoch 300\n",
+      "-------------------------------\n",
+      "loss: 0.600598  [   12/  240]\n",
+      "loss: 8.828585  [  132/  240]\n",
+      "Epoch 301\n",
+      "-------------------------------\n",
+      "loss: 24.764334  [   12/  240]\n",
+      "loss: 0.649912  [  132/  240]\n",
+      "Epoch 302\n",
+      "-------------------------------\n",
+      "loss: 0.594229  [   12/  240]\n",
+      "loss: 0.619455  [  132/  240]\n",
+      "Epoch 303\n",
+      "-------------------------------\n",
+      "loss: 8.484115  [   12/  240]\n",
+      "loss: 0.639304  [  132/  240]\n",
+      "Epoch 304\n",
+      "-------------------------------\n",
+      "loss: 0.602763  [   12/  240]\n",
+      "loss: 8.677493  [  132/  240]\n",
+      "Epoch 305\n",
+      "-------------------------------\n",
+      "loss: 0.605381  [   12/  240]\n",
+      "loss: 0.613512  [  132/  240]\n",
+      "Epoch 306\n",
+      "-------------------------------\n",
+      "loss: 0.612650  [   12/  240]\n",
+      "loss: 24.746752  [  132/  240]\n",
+      "Epoch 307\n",
+      "-------------------------------\n",
+      "loss: 0.627606  [   12/  240]\n",
+      "loss: 0.654974  [  132/  240]\n",
+      "Epoch 308\n",
+      "-------------------------------\n",
+      "loss: 0.631905  [   12/  240]\n",
+      "loss: 0.617928  [  132/  240]\n",
+      "Epoch 309\n",
+      "-------------------------------\n",
+      "loss: 0.625869  [   12/  240]\n",
+      "loss: 0.629607  [  132/  240]\n",
+      "Epoch 310\n",
+      "-------------------------------\n",
+      "loss: 0.599131  [   12/  240]\n",
+      "loss: 0.619500  [  132/  240]\n",
+      "Epoch 311\n",
+      "-------------------------------\n",
+      "loss: 0.614791  [   12/  240]\n",
+      "loss: 18.978064  [  132/  240]\n",
+      "Epoch 312\n",
+      "-------------------------------\n",
+      "loss: 0.668590  [   12/  240]\n",
+      "loss: 0.633764  [  132/  240]\n",
+      "Epoch 313\n",
+      "-------------------------------\n",
+      "loss: 0.643067  [   12/  240]\n",
+      "loss: 0.638205  [  132/  240]\n",
+      "Epoch 314\n",
+      "-------------------------------\n",
+      "loss: 0.607709  [   12/  240]\n",
+      "loss: 0.582640  [  132/  240]\n",
+      "Epoch 315\n",
+      "-------------------------------\n",
+      "loss: 0.597713  [   12/  240]\n",
+      "loss: 0.621113  [  132/  240]\n",
+      "Epoch 316\n",
+      "-------------------------------\n",
+      "loss: 0.607509  [   12/  240]\n",
+      "loss: 0.633206  [  132/  240]\n",
+      "Epoch 317\n",
+      "-------------------------------\n",
+      "loss: 0.648667  [   12/  240]\n",
+      "loss: 0.621142  [  132/  240]\n",
+      "Epoch 318\n",
+      "-------------------------------\n",
+      "loss: 26.324247  [   12/  240]\n",
+      "loss: 0.639830  [  132/  240]\n",
+      "Epoch 319\n",
+      "-------------------------------\n",
+      "loss: 0.619462  [   12/  240]\n",
+      "loss: 0.630695  [  132/  240]\n",
+      "Epoch 320\n",
+      "-------------------------------\n",
+      "loss: 0.606206  [   12/  240]\n",
+      "loss: 18.937050  [  132/  240]\n",
+      "Epoch 321\n",
+      "-------------------------------\n",
+      "loss: 0.608881  [   12/  240]\n",
+      "loss: 18.952307  [  132/  240]\n",
+      "Epoch 322\n",
+      "-------------------------------\n",
+      "loss: 26.291006  [   12/  240]\n",
+      "loss: 0.637594  [  132/  240]\n",
+      "Epoch 323\n",
+      "-------------------------------\n",
+      "loss: 0.620768  [   12/  240]\n",
+      "loss: 0.617506  [  132/  240]\n",
+      "Epoch 324\n",
+      "-------------------------------\n",
+      "loss: 0.610393  [   12/  240]\n",
+      "loss: 0.627661  [  132/  240]\n",
+      "Epoch 325\n",
+      "-------------------------------\n",
+      "loss: 0.600676  [   12/  240]\n",
+      "loss: 0.590579  [  132/  240]\n",
+      "Epoch 326\n",
+      "-------------------------------\n",
+      "loss: 0.594302  [   12/  240]\n",
+      "loss: 8.039950  [  132/  240]\n",
+      "Epoch 327\n",
+      "-------------------------------\n",
+      "loss: 24.749659  [   12/  240]\n",
+      "loss: 0.665101  [  132/  240]\n",
+      "Epoch 328\n",
+      "-------------------------------\n",
+      "loss: 0.608280  [   12/  240]\n",
+      "loss: 0.618249  [  132/  240]\n",
+      "Epoch 329\n",
+      "-------------------------------\n",
+      "loss: 0.605629  [   12/  240]\n",
+      "loss: 0.613430  [  132/  240]\n",
+      "Epoch 330\n",
+      "-------------------------------\n",
+      "loss: 0.604363  [   12/  240]\n",
+      "loss: 24.756413  [  132/  240]\n",
+      "Epoch 331\n",
+      "-------------------------------\n",
+      "loss: 0.616642  [   12/  240]\n",
+      "loss: 0.628463  [  132/  240]\n",
+      "Epoch 332\n",
+      "-------------------------------\n",
+      "loss: 0.614318  [   12/  240]\n",
+      "loss: 0.582420  [  132/  240]\n",
+      "Epoch 333\n",
+      "-------------------------------\n",
+      "loss: 0.627996  [   12/  240]\n",
+      "loss: 0.614376  [  132/  240]\n",
+      "Epoch 334\n",
+      "-------------------------------\n",
+      "loss: 0.601028  [   12/  240]\n",
+      "loss: 0.615321  [  132/  240]\n",
+      "Epoch 335\n",
+      "-------------------------------\n",
+      "loss: 0.607474  [   12/  240]\n",
+      "loss: 0.598728  [  132/  240]\n",
+      "Epoch 336\n",
+      "-------------------------------\n",
+      "loss: 0.621706  [   12/  240]\n",
+      "loss: 0.599417  [  132/  240]\n",
+      "Epoch 337\n",
+      "-------------------------------\n",
+      "loss: 0.589693  [   12/  240]\n",
+      "loss: 18.932713  [  132/  240]\n",
+      "Epoch 338\n",
+      "-------------------------------\n",
+      "loss: 0.585295  [   12/  240]\n",
+      "loss: 0.623519  [  132/  240]\n",
+      "Epoch 339\n",
+      "-------------------------------\n",
+      "loss: 0.637644  [   12/  240]\n",
+      "loss: 0.635684  [  132/  240]\n",
+      "Epoch 340\n",
+      "-------------------------------\n",
+      "loss: 0.629953  [   12/  240]\n",
+      "loss: 0.604195  [  132/  240]\n",
+      "Epoch 341\n",
+      "-------------------------------\n",
+      "loss: 0.610362  [   12/  240]\n",
+      "loss: 0.599137  [  132/  240]\n",
+      "Epoch 342\n",
+      "-------------------------------\n",
+      "loss: 0.609196  [   12/  240]\n",
+      "loss: 0.616385  [  132/  240]\n",
+      "Epoch 343\n",
+      "-------------------------------\n",
+      "loss: 0.612134  [   12/  240]\n",
+      "loss: 0.587615  [  132/  240]\n",
+      "Epoch 344\n",
+      "-------------------------------\n",
+      "loss: 0.607224  [   12/  240]\n",
+      "loss: 7.435689  [  132/  240]\n",
+      "Epoch 345\n",
+      "-------------------------------\n",
+      "loss: 24.761469  [   12/  240]\n",
+      "loss: 18.917290  [  132/  240]\n",
+      "Epoch 346\n",
+      "-------------------------------\n",
+      "loss: 0.602730  [   12/  240]\n",
+      "loss: 0.584788  [  132/  240]\n",
+      "Epoch 347\n",
+      "-------------------------------\n",
+      "loss: 0.615322  [   12/  240]\n",
+      "loss: 0.610333  [  132/  240]\n",
+      "Epoch 348\n",
+      "-------------------------------\n",
+      "loss: 0.598875  [   12/  240]\n",
+      "loss: 0.600658  [  132/  240]\n",
+      "Epoch 349\n",
+      "-------------------------------\n",
+      "loss: 0.627032  [   12/  240]\n",
+      "loss: 0.639747  [  132/  240]\n",
+      "Epoch 350\n",
+      "-------------------------------\n",
+      "loss: 25.299820  [   12/  240]\n",
+      "loss: 0.635565  [  132/  240]\n",
+      "Epoch 351\n",
+      "-------------------------------\n",
+      "loss: 0.619946  [   12/  240]\n",
+      "loss: 7.288666  [  132/  240]\n",
+      "Epoch 352\n",
+      "-------------------------------\n",
+      "loss: 0.625632  [   12/  240]\n",
+      "loss: 0.617127  [  132/  240]\n",
+      "Epoch 353\n",
+      "-------------------------------\n",
+      "loss: 0.608222  [   12/  240]\n",
+      "loss: 0.631180  [  132/  240]\n",
+      "Epoch 354\n",
+      "-------------------------------\n",
+      "loss: 0.619135  [   12/  240]\n",
+      "loss: 0.593061  [  132/  240]\n",
+      "Epoch 355\n",
+      "-------------------------------\n",
+      "loss: 0.608599  [   12/  240]\n",
+      "loss: 0.612600  [  132/  240]\n",
+      "Epoch 356\n",
+      "-------------------------------\n",
+      "loss: 0.611287  [   12/  240]\n",
+      "loss: 0.609664  [  132/  240]\n",
+      "Epoch 357\n",
+      "-------------------------------\n",
+      "loss: 6.968694  [   12/  240]\n",
+      "loss: 0.644863  [  132/  240]\n",
+      "Epoch 358\n",
+      "-------------------------------\n",
+      "loss: 0.604020  [   12/  240]\n",
+      "loss: 0.635344  [  132/  240]\n",
+      "Epoch 359\n",
+      "-------------------------------\n",
+      "loss: 6.852080  [   12/  240]\n",
+      "loss: 0.625168  [  132/  240]\n",
+      "Epoch 360\n",
+      "-------------------------------\n",
+      "loss: 24.718542  [   12/  240]\n",
+      "loss: 0.593957  [  132/  240]\n",
+      "Epoch 361\n",
+      "-------------------------------\n",
+      "loss: 6.779389  [   12/  240]\n",
+      "loss: 0.634529  [  132/  240]\n",
+      "Epoch 362\n",
+      "-------------------------------\n",
+      "loss: 0.625451  [   12/  240]\n",
+      "loss: 7.081840  [  132/  240]\n",
+      "Epoch 363\n",
+      "-------------------------------\n",
+      "loss: 0.592315  [   12/  240]\n",
+      "loss: 0.598447  [  132/  240]\n",
+      "Epoch 364\n",
+      "-------------------------------\n",
+      "loss: 0.598143  [   12/  240]\n",
+      "loss: 0.595892  [  132/  240]\n",
+      "Epoch 365\n",
+      "-------------------------------\n",
+      "loss: 24.759027  [   12/  240]\n",
+      "loss: 0.616910  [  132/  240]\n",
+      "Epoch 366\n",
+      "-------------------------------\n",
+      "loss: 0.630606  [   12/  240]\n",
+      "loss: 0.605392  [  132/  240]\n",
+      "Epoch 367\n",
+      "-------------------------------\n",
+      "loss: 24.749294  [   12/  240]\n",
+      "loss: 0.599434  [  132/  240]\n",
+      "Epoch 368\n",
+      "-------------------------------\n",
+      "loss: 0.582448  [   12/  240]\n",
+      "loss: 0.591097  [  132/  240]\n",
+      "Epoch 369\n",
+      "-------------------------------\n",
+      "loss: 0.638861  [   12/  240]\n",
+      "loss: 0.635578  [  132/  240]\n",
+      "Epoch 370\n",
+      "-------------------------------\n",
+      "loss: 0.645926  [   12/  240]\n",
+      "loss: 0.606159  [  132/  240]\n",
+      "Epoch 371\n",
+      "-------------------------------\n",
+      "loss: 0.615996  [   12/  240]\n",
+      "loss: 0.598351  [  132/  240]\n",
+      "Epoch 372\n",
+      "-------------------------------\n",
+      "loss: 0.665528  [   12/  240]\n",
+      "loss: 0.611773  [  132/  240]\n",
+      "Epoch 373\n",
+      "-------------------------------\n",
+      "loss: 0.614618  [   12/  240]\n",
+      "loss: 0.608608  [  132/  240]\n",
+      "Epoch 374\n",
+      "-------------------------------\n",
+      "loss: 0.603575  [   12/  240]\n",
+      "loss: 0.589411  [  132/  240]\n",
+      "Epoch 375\n",
+      "-------------------------------\n",
+      "loss: 0.616036  [   12/  240]\n",
+      "loss: 0.588093  [  132/  240]\n",
+      "Epoch 376\n",
+      "-------------------------------\n",
+      "loss: 0.614455  [   12/  240]\n",
+      "loss: 0.627393  [  132/  240]\n",
+      "Epoch 377\n",
+      "-------------------------------\n",
+      "loss: 0.610416  [   12/  240]\n",
+      "loss: 0.591026  [  132/  240]\n",
+      "Epoch 378\n",
+      "-------------------------------\n",
+      "loss: 0.611406  [   12/  240]\n",
+      "loss: 0.613046  [  132/  240]\n",
+      "Epoch 379\n",
+      "-------------------------------\n",
+      "loss: 0.626646  [   12/  240]\n",
+      "loss: 0.629424  [  132/  240]\n",
+      "Epoch 380\n",
+      "-------------------------------\n",
+      "loss: 0.634635  [   12/  240]\n",
+      "loss: 0.641131  [  132/  240]\n",
+      "Epoch 381\n",
+      "-------------------------------\n",
+      "loss: 0.613841  [   12/  240]\n",
+      "loss: 0.654077  [  132/  240]\n",
+      "Epoch 382\n",
+      "-------------------------------\n",
+      "loss: 0.612892  [   12/  240]\n",
+      "loss: 18.917419  [  132/  240]\n",
+      "Epoch 383\n",
+      "-------------------------------\n",
+      "loss: 0.607974  [   12/  240]\n",
+      "loss: 0.600052  [  132/  240]\n",
+      "Epoch 384\n",
+      "-------------------------------\n",
+      "loss: 5.909238  [   12/  240]\n",
+      "loss: 0.631279  [  132/  240]\n",
+      "Epoch 385\n",
+      "-------------------------------\n",
+      "loss: 0.613196  [   12/  240]\n",
+      "loss: 0.594699  [  132/  240]\n",
+      "Epoch 386\n",
+      "-------------------------------\n",
+      "loss: 0.594461  [   12/  240]\n",
+      "loss: 0.608844  [  132/  240]\n",
+      "Epoch 387\n",
+      "-------------------------------\n",
+      "loss: 0.611817  [   12/  240]\n",
+      "loss: 0.632424  [  132/  240]\n",
+      "Epoch 388\n",
+      "-------------------------------\n",
+      "loss: 5.835342  [   12/  240]\n",
+      "loss: 0.630950  [  132/  240]\n",
+      "Epoch 389\n",
+      "-------------------------------\n",
+      "loss: 0.595672  [   12/  240]\n",
+      "loss: 0.596678  [  132/  240]\n",
+      "Epoch 390\n",
+      "-------------------------------\n",
+      "loss: 0.598956  [   12/  240]\n",
+      "loss: 0.607481  [  132/  240]\n",
+      "Epoch 391\n",
+      "-------------------------------\n",
+      "loss: 0.583035  [   12/  240]\n",
+      "loss: 0.578154  [  132/  240]\n",
+      "Epoch 392\n",
+      "-------------------------------\n",
+      "loss: 0.615045  [   12/  240]\n",
+      "loss: 0.626111  [  132/  240]\n",
+      "Epoch 393\n",
+      "-------------------------------\n",
+      "loss: 0.613785  [   12/  240]\n",
+      "loss: 0.610530  [  132/  240]\n",
+      "Epoch 394\n",
+      "-------------------------------\n",
+      "loss: 0.601282  [   12/  240]\n",
+      "loss: 0.622521  [  132/  240]\n",
+      "Epoch 395\n",
+      "-------------------------------\n",
+      "loss: 24.770334  [   12/  240]\n",
+      "loss: 18.948490  [  132/  240]\n",
+      "Epoch 396\n",
+      "-------------------------------\n",
+      "loss: 0.602931  [   12/  240]\n",
+      "loss: 0.624849  [  132/  240]\n",
+      "Epoch 397\n",
+      "-------------------------------\n",
+      "loss: 0.608305  [   12/  240]\n",
+      "loss: 0.630821  [  132/  240]\n",
+      "Epoch 398\n",
+      "-------------------------------\n",
+      "loss: 0.615496  [   12/  240]\n",
+      "loss: 0.604327  [  132/  240]\n",
+      "Epoch 399\n",
+      "-------------------------------\n",
+      "loss: 0.597060  [   12/  240]\n",
+      "loss: 0.585397  [  132/  240]\n",
+      "Epoch 400\n",
+      "-------------------------------\n",
+      "loss: 0.602813  [   12/  240]\n",
+      "loss: 0.583576  [  132/  240]\n",
+      "Epoch 401\n",
+      "-------------------------------\n",
+      "loss: 0.597760  [   12/  240]\n",
+      "loss: 0.609807  [  132/  240]\n",
+      "Epoch 402\n",
+      "-------------------------------\n",
+      "loss: 0.616104  [   12/  240]\n",
+      "loss: 0.627729  [  132/  240]\n",
+      "Epoch 403\n",
+      "-------------------------------\n",
+      "loss: 0.611239  [   12/  240]\n",
+      "loss: 0.599676  [  132/  240]\n",
+      "Epoch 404\n",
+      "-------------------------------\n",
+      "loss: 24.732393  [   12/  240]\n",
+      "loss: 0.598976  [  132/  240]\n",
+      "Epoch 405\n",
+      "-------------------------------\n",
+      "loss: 0.582671  [   12/  240]\n",
+      "loss: 0.599647  [  132/  240]\n",
+      "Epoch 406\n",
+      "-------------------------------\n",
+      "loss: 0.603513  [   12/  240]\n",
+      "loss: 0.610758  [  132/  240]\n",
+      "Epoch 407\n",
+      "-------------------------------\n",
+      "loss: 0.596378  [   12/  240]\n",
+      "loss: 0.595986  [  132/  240]\n",
+      "Epoch 408\n",
+      "-------------------------------\n",
+      "loss: 0.598671  [   12/  240]\n",
+      "loss: 0.604046  [  132/  240]\n",
+      "Epoch 409\n",
+      "-------------------------------\n",
+      "loss: 24.723949  [   12/  240]\n",
+      "loss: 5.160507  [  132/  240]\n",
+      "Epoch 410\n",
+      "-------------------------------\n",
+      "loss: 0.613985  [   12/  240]\n",
+      "loss: 0.596963  [  132/  240]\n",
+      "Epoch 411\n",
+      "-------------------------------\n",
+      "loss: 0.599723  [   12/  240]\n",
+      "loss: 0.631401  [  132/  240]\n",
+      "Epoch 412\n",
+      "-------------------------------\n",
+      "loss: 0.615869  [   12/  240]\n",
+      "loss: 0.611210  [  132/  240]\n",
+      "Epoch 413\n",
+      "-------------------------------\n",
+      "loss: 4.963411  [   12/  240]\n",
+      "loss: 0.625285  [  132/  240]\n",
+      "Epoch 414\n",
+      "-------------------------------\n",
+      "loss: 18.924135  [   12/  240]\n",
+      "loss: 0.589602  [  132/  240]\n",
+      "Epoch 415\n",
+      "-------------------------------\n",
+      "loss: 24.735170  [   12/  240]\n",
+      "loss: 0.612498  [  132/  240]\n",
+      "Epoch 416\n",
+      "-------------------------------\n",
+      "loss: 0.590839  [   12/  240]\n",
+      "loss: 0.605934  [  132/  240]\n",
+      "Epoch 417\n",
+      "-------------------------------\n",
+      "loss: 0.598024  [   12/  240]\n",
+      "loss: 0.602420  [  132/  240]\n",
+      "Epoch 418\n",
+      "-------------------------------\n",
+      "loss: 0.614516  [   12/  240]\n",
+      "loss: 0.609145  [  132/  240]\n",
+      "Epoch 419\n",
+      "-------------------------------\n",
+      "loss: 0.639863  [   12/  240]\n",
+      "loss: 0.602278  [  132/  240]\n",
+      "Epoch 420\n",
+      "-------------------------------\n",
+      "loss: 0.620946  [   12/  240]\n",
+      "loss: 0.599561  [  132/  240]\n",
+      "Epoch 421\n",
+      "-------------------------------\n",
+      "loss: 0.602412  [   12/  240]\n",
+      "loss: 0.609126  [  132/  240]\n",
+      "Epoch 422\n",
+      "-------------------------------\n",
+      "loss: 0.617636  [   12/  240]\n",
+      "loss: 24.762964  [  132/  240]\n",
+      "Epoch 423\n",
+      "-------------------------------\n",
+      "loss: 0.608936  [   12/  240]\n",
+      "loss: 0.605764  [  132/  240]\n",
+      "Epoch 424\n",
+      "-------------------------------\n",
+      "loss: 0.608954  [   12/  240]\n",
+      "loss: 0.605042  [  132/  240]\n",
+      "Epoch 425\n",
+      "-------------------------------\n",
+      "loss: 0.608133  [   12/  240]\n",
+      "loss: 0.590063  [  132/  240]\n",
+      "Epoch 426\n",
+      "-------------------------------\n",
+      "loss: 24.726059  [   12/  240]\n",
+      "loss: 0.601542  [  132/  240]\n",
+      "Epoch 427\n",
+      "-------------------------------\n",
+      "loss: 0.609772  [   12/  240]\n",
+      "loss: 4.505234  [  132/  240]\n",
+      "Epoch 428\n",
+      "-------------------------------\n",
+      "loss: 0.588826  [   12/  240]\n",
+      "loss: 0.596931  [  132/  240]\n",
+      "Epoch 429\n",
+      "-------------------------------\n",
+      "loss: 24.756088  [   12/  240]\n",
+      "loss: 0.619817  [  132/  240]\n",
+      "Epoch 430\n",
+      "-------------------------------\n",
+      "loss: 0.609265  [   12/  240]\n",
+      "loss: 0.576139  [  132/  240]\n",
+      "Epoch 431\n",
+      "-------------------------------\n",
+      "loss: 18.922239  [   12/  240]\n",
+      "loss: 0.603662  [  132/  240]\n",
+      "Epoch 432\n",
+      "-------------------------------\n",
+      "loss: 0.590340  [   12/  240]\n",
+      "loss: 0.605262  [  132/  240]\n",
+      "Epoch 433\n",
+      "-------------------------------\n",
+      "loss: 0.607453  [   12/  240]\n",
+      "loss: 0.613419  [  132/  240]\n",
+      "Epoch 434\n",
+      "-------------------------------\n",
+      "loss: 0.612802  [   12/  240]\n",
+      "loss: 0.603138  [  132/  240]\n",
+      "Epoch 435\n",
+      "-------------------------------\n",
+      "loss: 0.583034  [   12/  240]\n",
+      "loss: 0.603744  [  132/  240]\n",
+      "Epoch 436\n",
+      "-------------------------------\n",
+      "loss: 0.611940  [   12/  240]\n",
+      "loss: 0.619355  [  132/  240]\n",
+      "Epoch 437\n",
+      "-------------------------------\n",
+      "loss: 0.594169  [   12/  240]\n",
+      "loss: 0.618763  [  132/  240]\n",
+      "Epoch 438\n",
+      "-------------------------------\n",
+      "loss: 0.603896  [   12/  240]\n",
+      "loss: 0.592818  [  132/  240]\n",
+      "Epoch 439\n",
+      "-------------------------------\n",
+      "loss: 0.607276  [   12/  240]\n",
+      "loss: 0.605204  [  132/  240]\n",
+      "Epoch 440\n",
+      "-------------------------------\n",
+      "loss: 0.608515  [   12/  240]\n",
+      "loss: 0.580257  [  132/  240]\n",
+      "Epoch 441\n",
+      "-------------------------------\n",
+      "loss: 3.627962  [   12/  240]\n",
+      "loss: 0.618797  [  132/  240]\n",
+      "Epoch 442\n",
+      "-------------------------------\n",
+      "loss: 0.598710  [   12/  240]\n",
+      "loss: 0.620280  [  132/  240]\n",
+      "Epoch 443\n",
+      "-------------------------------\n",
+      "loss: 0.599244  [   12/  240]\n",
+      "loss: 0.580478  [  132/  240]\n",
+      "Epoch 444\n",
+      "-------------------------------\n",
+      "loss: 0.588673  [   12/  240]\n",
+      "loss: 0.614850  [  132/  240]\n",
+      "Epoch 445\n",
+      "-------------------------------\n",
+      "loss: 0.609122  [   12/  240]\n",
+      "loss: 0.594663  [  132/  240]\n",
+      "Epoch 446\n",
+      "-------------------------------\n",
+      "loss: 0.614849  [   12/  240]\n",
+      "loss: 0.607431  [  132/  240]\n",
+      "Epoch 447\n",
+      "-------------------------------\n",
+      "loss: 0.618283  [   12/  240]\n",
+      "loss: 0.590339  [  132/  240]\n",
+      "Epoch 448\n",
+      "-------------------------------\n",
+      "loss: 0.579180  [   12/  240]\n",
+      "loss: 0.595436  [  132/  240]\n",
+      "Epoch 449\n",
+      "-------------------------------\n",
+      "loss: 0.578198  [   12/  240]\n",
+      "loss: 0.580165  [  132/  240]\n",
+      "Epoch 450\n",
+      "-------------------------------\n",
+      "loss: 18.945358  [   12/  240]\n",
+      "loss: 0.598497  [  132/  240]\n",
+      "Epoch 451\n",
+      "-------------------------------\n",
+      "loss: 0.581336  [   12/  240]\n",
+      "loss: 0.604635  [  132/  240]\n",
+      "Epoch 452\n",
+      "-------------------------------\n",
+      "loss: 0.583976  [   12/  240]\n",
+      "loss: 0.587723  [  132/  240]\n",
+      "Epoch 453\n",
+      "-------------------------------\n",
+      "loss: 0.594764  [   12/  240]\n",
+      "loss: 18.952789  [  132/  240]\n",
+      "Epoch 454\n",
+      "-------------------------------\n",
+      "loss: 0.584319  [   12/  240]\n",
+      "loss: 0.592186  [  132/  240]\n",
+      "Epoch 455\n",
+      "-------------------------------\n",
+      "loss: 0.606394  [   12/  240]\n",
+      "loss: 0.590978  [  132/  240]\n",
+      "Epoch 456\n",
+      "-------------------------------\n",
+      "loss: 0.583819  [   12/  240]\n",
+      "loss: 0.589754  [  132/  240]\n",
+      "Epoch 457\n",
+      "-------------------------------\n",
+      "loss: 0.591226  [   12/  240]\n",
+      "loss: 0.605751  [  132/  240]\n",
+      "Epoch 458\n",
+      "-------------------------------\n",
+      "loss: 0.624212  [   12/  240]\n",
+      "loss: 0.593513  [  132/  240]\n",
+      "Epoch 459\n",
+      "-------------------------------\n",
+      "loss: 0.604703  [   12/  240]\n",
+      "loss: 0.586854  [  132/  240]\n",
+      "Epoch 460\n",
+      "-------------------------------\n",
+      "loss: 0.573446  [   12/  240]\n",
+      "loss: 0.586842  [  132/  240]\n",
+      "Epoch 461\n",
+      "-------------------------------\n",
+      "loss: 2.458293  [   12/  240]\n",
+      "loss: 0.588856  [  132/  240]\n",
+      "Epoch 462\n",
+      "-------------------------------\n",
+      "loss: 0.611502  [   12/  240]\n",
+      "loss: 0.583516  [  132/  240]\n",
+      "Epoch 463\n",
+      "-------------------------------\n",
+      "loss: 24.721165  [   12/  240]\n",
+      "loss: 0.597980  [  132/  240]\n",
+      "Epoch 464\n",
+      "-------------------------------\n",
+      "loss: 0.615565  [   12/  240]\n",
+      "loss: 24.739925  [  132/  240]\n",
+      "Epoch 465\n",
+      "-------------------------------\n",
+      "loss: 0.589491  [   12/  240]\n",
+      "loss: 24.684555  [  132/  240]\n",
+      "Epoch 466\n",
+      "-------------------------------\n",
+      "loss: 0.585727  [   12/  240]\n",
+      "loss: 0.564576  [  132/  240]\n",
+      "Epoch 467\n",
+      "-------------------------------\n",
+      "loss: 0.586995  [   12/  240]\n",
+      "loss: 0.603146  [  132/  240]\n",
+      "Epoch 468\n",
+      "-------------------------------\n",
+      "loss: 24.731737  [   12/  240]\n",
+      "loss: 0.600925  [  132/  240]\n",
+      "Epoch 469\n",
+      "-------------------------------\n",
+      "loss: 0.588365  [   12/  240]\n",
+      "loss: 0.602603  [  132/  240]\n",
+      "Epoch 470\n",
+      "-------------------------------\n",
+      "loss: 0.569879  [   12/  240]\n",
+      "loss: 0.585662  [  132/  240]\n",
+      "Epoch 471\n",
+      "-------------------------------\n",
+      "loss: 0.611479  [   12/  240]\n",
+      "loss: 0.564782  [  132/  240]\n",
+      "Epoch 472\n",
+      "-------------------------------\n",
+      "loss: 0.562651  [   12/  240]\n",
+      "loss: 0.593876  [  132/  240]\n",
+      "Epoch 473\n",
+      "-------------------------------\n",
+      "loss: 0.596423  [   12/  240]\n",
+      "loss: 0.593463  [  132/  240]\n",
+      "Epoch 474\n",
+      "-------------------------------\n",
+      "loss: 0.569298  [   12/  240]\n",
+      "loss: 0.593270  [  132/  240]\n",
+      "Epoch 475\n",
+      "-------------------------------\n",
+      "loss: 0.581669  [   12/  240]\n",
+      "loss: 18.958549  [  132/  240]\n",
+      "Epoch 476\n",
+      "-------------------------------\n",
+      "loss: 0.615230  [   12/  240]\n",
+      "loss: 1.951588  [  132/  240]\n",
+      "Epoch 477\n",
+      "-------------------------------\n",
+      "loss: 1.827251  [   12/  240]\n",
+      "loss: 0.606359  [  132/  240]\n",
+      "Epoch 478\n",
+      "-------------------------------\n",
+      "loss: 0.575752  [   12/  240]\n",
+      "loss: 0.585672  [  132/  240]\n",
+      "Epoch 479\n",
+      "-------------------------------\n",
+      "loss: 0.587757  [   12/  240]\n",
+      "loss: 0.604106  [  132/  240]\n",
+      "Epoch 480\n",
+      "-------------------------------\n",
+      "loss: 18.923117  [   12/  240]\n",
+      "loss: 0.601799  [  132/  240]\n",
+      "Epoch 481\n",
+      "-------------------------------\n",
+      "loss: 0.585481  [   12/  240]\n",
+      "loss: 1.833372  [  132/  240]\n",
+      "Epoch 482\n",
+      "-------------------------------\n",
+      "loss: 0.585086  [   12/  240]\n",
+      "loss: 18.933453  [  132/  240]\n",
+      "Epoch 483\n",
+      "-------------------------------\n",
+      "loss: 0.579978  [   12/  240]\n",
+      "loss: 18.947023  [  132/  240]\n",
+      "Epoch 484\n",
+      "-------------------------------\n",
+      "loss: 0.569390  [   12/  240]\n",
+      "loss: 0.600477  [  132/  240]\n",
+      "Epoch 485\n",
+      "-------------------------------\n",
+      "loss: 0.592954  [   12/  240]\n",
+      "loss: 0.581596  [  132/  240]\n",
+      "Epoch 486\n",
+      "-------------------------------\n",
+      "loss: 0.579900  [   12/  240]\n",
+      "loss: 0.564317  [  132/  240]\n",
+      "Epoch 487\n",
+      "-------------------------------\n",
+      "loss: 0.575155  [   12/  240]\n",
+      "loss: 0.581881  [  132/  240]\n",
+      "Epoch 488\n",
+      "-------------------------------\n",
+      "loss: 0.581205  [   12/  240]\n",
+      "loss: 0.597450  [  132/  240]\n",
+      "Epoch 489\n",
+      "-------------------------------\n",
+      "loss: 0.603576  [   12/  240]\n",
+      "loss: 0.583088  [  132/  240]\n",
+      "Epoch 490\n",
+      "-------------------------------\n",
+      "loss: 0.562192  [   12/  240]\n",
+      "loss: 0.573123  [  132/  240]\n",
+      "Epoch 491\n",
+      "-------------------------------\n",
+      "loss: 0.582953  [   12/  240]\n",
+      "loss: 1.575815  [  132/  240]\n",
+      "Epoch 492\n",
+      "-------------------------------\n",
+      "loss: 0.585805  [   12/  240]\n",
+      "loss: 0.590296  [  132/  240]\n",
+      "Epoch 493\n",
+      "-------------------------------\n",
+      "loss: 0.578619  [   12/  240]\n",
+      "loss: 0.589065  [  132/  240]\n",
+      "Epoch 494\n",
+      "-------------------------------\n",
+      "loss: 18.932224  [   12/  240]\n",
+      "loss: 0.581454  [  132/  240]\n",
+      "Epoch 495\n",
+      "-------------------------------\n",
+      "loss: 0.590305  [   12/  240]\n",
+      "loss: 0.603043  [  132/  240]\n",
+      "Epoch 496\n",
+      "-------------------------------\n",
+      "loss: 24.717644  [   12/  240]\n",
+      "loss: 0.574570  [  132/  240]\n",
+      "Epoch 497\n",
+      "-------------------------------\n",
+      "loss: 0.586782  [   12/  240]\n",
+      "loss: 0.588801  [  132/  240]\n",
+      "Epoch 498\n",
+      "-------------------------------\n",
+      "loss: 18.914824  [   12/  240]\n",
+      "loss: 0.610434  [  132/  240]\n",
+      "Epoch 499\n",
+      "-------------------------------\n",
+      "loss: 24.718626  [   12/  240]\n",
+      "loss: 0.599000  [  132/  240]\n",
+      "Epoch 500\n",
+      "-------------------------------\n",
+      "loss: 0.593102  [   12/  240]\n",
+      "loss: 0.549969  [  132/  240]\n",
+      "Epoch 501\n",
+      "-------------------------------\n",
+      "loss: 24.736954  [   12/  240]\n",
+      "loss: 0.600369  [  132/  240]\n",
+      "Epoch 502\n",
+      "-------------------------------\n",
+      "loss: 1.296115  [   12/  240]\n",
+      "loss: 0.594531  [  132/  240]\n",
+      "Epoch 503\n",
+      "-------------------------------\n",
+      "loss: 0.581008  [   12/  240]\n",
+      "loss: 0.604422  [  132/  240]\n",
+      "Epoch 504\n",
+      "-------------------------------\n",
+      "loss: 0.571531  [   12/  240]\n",
+      "loss: 1.512788  [  132/  240]\n",
+      "Epoch 505\n",
+      "-------------------------------\n",
+      "loss: 0.585755  [   12/  240]\n",
+      "loss: 0.617533  [  132/  240]\n",
+      "Epoch 506\n",
+      "-------------------------------\n",
+      "loss: 0.588666  [   12/  240]\n",
+      "loss: 0.575918  [  132/  240]\n",
+      "Epoch 507\n",
+      "-------------------------------\n",
+      "loss: 0.562347  [   12/  240]\n",
+      "loss: 0.595504  [  132/  240]\n",
+      "Epoch 508\n",
+      "-------------------------------\n",
+      "loss: 0.586930  [   12/  240]\n",
+      "loss: 0.573587  [  132/  240]\n",
+      "Epoch 509\n",
+      "-------------------------------\n",
+      "loss: 0.615293  [   12/  240]\n",
+      "loss: 0.597750  [  132/  240]\n",
+      "Epoch 510\n",
+      "-------------------------------\n",
+      "loss: 0.579401  [   12/  240]\n",
+      "loss: 0.586076  [  132/  240]\n",
+      "Epoch 511\n",
+      "-------------------------------\n",
+      "loss: 0.589467  [   12/  240]\n",
+      "loss: 18.940256  [  132/  240]\n",
+      "Epoch 512\n",
+      "-------------------------------\n",
+      "loss: 25.486122  [   12/  240]\n",
+      "loss: 0.610332  [  132/  240]\n",
+      "Epoch 513\n",
+      "-------------------------------\n",
+      "loss: 0.593553  [   12/  240]\n",
+      "loss: 0.585451  [  132/  240]\n",
+      "Epoch 514\n",
+      "-------------------------------\n",
+      "loss: 0.570772  [   12/  240]\n",
+      "loss: 1.406354  [  132/  240]\n",
+      "Epoch 515\n",
+      "-------------------------------\n",
+      "loss: 0.590529  [   12/  240]\n",
+      "loss: 0.604937  [  132/  240]\n",
+      "Epoch 516\n",
+      "-------------------------------\n",
+      "loss: 0.598497  [   12/  240]\n",
+      "loss: 0.591283  [  132/  240]\n",
+      "Epoch 517\n",
+      "-------------------------------\n",
+      "loss: 0.597542  [   12/  240]\n",
+      "loss: 0.595457  [  132/  240]\n",
+      "Epoch 518\n",
+      "-------------------------------\n",
+      "loss: 0.583669  [   12/  240]\n",
+      "loss: 0.574842  [  132/  240]\n",
+      "Epoch 519\n",
+      "-------------------------------\n",
+      "loss: 0.594844  [   12/  240]\n",
+      "loss: 0.609097  [  132/  240]\n",
+      "Epoch 520\n",
+      "-------------------------------\n",
+      "loss: 0.583217  [   12/  240]\n",
+      "loss: 0.580276  [  132/  240]\n",
+      "Epoch 521\n",
+      "-------------------------------\n",
+      "loss: 0.585353  [   12/  240]\n",
+      "loss: 24.698555  [  132/  240]\n",
+      "Epoch 522\n",
+      "-------------------------------\n",
+      "loss: 0.598545  [   12/  240]\n",
+      "loss: 0.610975  [  132/  240]\n",
+      "Epoch 523\n",
+      "-------------------------------\n",
+      "loss: 0.585230  [   12/  240]\n",
+      "loss: 0.599272  [  132/  240]\n",
+      "Epoch 524\n",
+      "-------------------------------\n",
+      "loss: 0.581122  [   12/  240]\n",
+      "loss: 18.914928  [  132/  240]\n",
+      "Epoch 525\n",
+      "-------------------------------\n",
+      "loss: 0.585925  [   12/  240]\n",
+      "loss: 0.597174  [  132/  240]\n",
+      "Epoch 526\n",
+      "-------------------------------\n",
+      "loss: 0.590383  [   12/  240]\n",
+      "loss: 0.590790  [  132/  240]\n",
+      "Epoch 527\n",
+      "-------------------------------\n",
+      "loss: 1.312120  [   12/  240]\n",
+      "loss: 0.583685  [  132/  240]\n",
+      "Epoch 528\n",
+      "-------------------------------\n",
+      "loss: 0.597990  [   12/  240]\n",
+      "loss: 0.559276  [  132/  240]\n",
+      "Epoch 529\n",
+      "-------------------------------\n",
+      "loss: 24.724810  [   12/  240]\n",
+      "loss: 0.597166  [  132/  240]\n",
+      "Epoch 530\n",
+      "-------------------------------\n",
+      "loss: 0.578955  [   12/  240]\n",
+      "loss: 0.595047  [  132/  240]\n",
+      "Epoch 531\n",
+      "-------------------------------\n",
+      "loss: 0.585379  [   12/  240]\n",
+      "loss: 24.736511  [  132/  240]\n",
+      "Epoch 532\n",
+      "-------------------------------\n",
+      "loss: 0.593039  [   12/  240]\n",
+      "loss: 0.575109  [  132/  240]\n",
+      "Epoch 533\n",
+      "-------------------------------\n",
+      "loss: 0.561792  [   12/  240]\n",
+      "loss: 0.588682  [  132/  240]\n",
+      "Epoch 534\n",
+      "-------------------------------\n",
+      "loss: 0.581109  [   12/  240]\n",
+      "loss: 0.598549  [  132/  240]\n",
+      "Epoch 535\n",
+      "-------------------------------\n",
+      "loss: 0.593596  [   12/  240]\n",
+      "loss: 0.564961  [  132/  240]\n",
+      "Epoch 536\n",
+      "-------------------------------\n",
+      "loss: 0.588925  [   12/  240]\n",
+      "loss: 0.588194  [  132/  240]\n",
+      "Epoch 537\n",
+      "-------------------------------\n",
+      "loss: 0.597418  [   12/  240]\n",
+      "loss: 0.585324  [  132/  240]\n",
+      "Epoch 538\n",
+      "-------------------------------\n",
+      "loss: 0.559322  [   12/  240]\n",
+      "loss: 1.411872  [  132/  240]\n",
+      "Epoch 539\n",
+      "-------------------------------\n",
+      "loss: 1.209714  [   12/  240]\n",
+      "loss: 24.746597  [  132/  240]\n",
+      "Epoch 540\n",
+      "-------------------------------\n",
+      "loss: 1.103875  [   12/  240]\n",
+      "loss: 24.745296  [  132/  240]\n",
+      "Epoch 541\n",
+      "-------------------------------\n",
+      "loss: 0.588109  [   12/  240]\n",
+      "loss: 0.585729  [  132/  240]\n",
+      "Epoch 542\n",
+      "-------------------------------\n",
+      "loss: 0.599299  [   12/  240]\n",
+      "loss: 0.550489  [  132/  240]\n",
+      "Epoch 543\n",
+      "-------------------------------\n",
+      "loss: 0.552601  [   12/  240]\n",
+      "loss: 18.907906  [  132/  240]\n",
+      "Epoch 544\n",
+      "-------------------------------\n",
+      "loss: 0.609042  [   12/  240]\n",
+      "loss: 0.568977  [  132/  240]\n",
+      "Epoch 545\n",
+      "-------------------------------\n",
+      "loss: 0.588524  [   12/  240]\n",
+      "loss: 24.733805  [  132/  240]\n",
+      "Epoch 546\n",
+      "-------------------------------\n",
+      "loss: 0.600804  [   12/  240]\n",
+      "loss: 0.580887  [  132/  240]\n",
+      "Epoch 547\n",
+      "-------------------------------\n",
+      "loss: 0.596243  [   12/  240]\n",
+      "loss: 0.576701  [  132/  240]\n",
+      "Epoch 548\n",
+      "-------------------------------\n",
+      "loss: 0.586493  [   12/  240]\n",
+      "loss: 0.581010  [  132/  240]\n",
+      "Epoch 549\n",
+      "-------------------------------\n",
+      "loss: 0.578773  [   12/  240]\n",
+      "loss: 0.586829  [  132/  240]\n",
+      "Epoch 550\n",
+      "-------------------------------\n",
+      "loss: 1.313218  [   12/  240]\n",
+      "loss: 0.614948  [  132/  240]\n",
+      "Epoch 551\n",
+      "-------------------------------\n",
+      "loss: 0.589453  [   12/  240]\n",
+      "loss: 0.586880  [  132/  240]\n",
+      "Epoch 552\n",
+      "-------------------------------\n",
+      "loss: 0.583321  [   12/  240]\n",
+      "loss: 0.590439  [  132/  240]\n",
+      "Epoch 553\n",
+      "-------------------------------\n",
+      "loss: 24.728035  [   12/  240]\n",
+      "loss: 0.561498  [  132/  240]\n",
+      "Epoch 554\n",
+      "-------------------------------\n",
+      "loss: 24.709543  [   12/  240]\n",
+      "loss: 0.579636  [  132/  240]\n",
+      "Epoch 555\n",
+      "-------------------------------\n",
+      "loss: 0.587908  [   12/  240]\n",
+      "loss: 0.572174  [  132/  240]\n",
+      "Epoch 556\n",
+      "-------------------------------\n",
+      "loss: 0.570844  [   12/  240]\n",
+      "loss: 0.612920  [  132/  240]\n",
+      "Epoch 557\n",
+      "-------------------------------\n",
+      "loss: 0.588436  [   12/  240]\n",
+      "loss: 0.599927  [  132/  240]\n",
+      "Epoch 558\n",
+      "-------------------------------\n",
+      "loss: 0.567565  [   12/  240]\n",
+      "loss: 0.588878  [  132/  240]\n",
+      "Epoch 559\n",
+      "-------------------------------\n",
+      "loss: 0.581165  [   12/  240]\n",
+      "loss: 24.758451  [  132/  240]\n",
+      "Epoch 560\n",
+      "-------------------------------\n",
+      "loss: 0.574626  [   12/  240]\n",
+      "loss: 0.569154  [  132/  240]\n",
+      "Epoch 561\n",
+      "-------------------------------\n",
+      "loss: 0.560668  [   12/  240]\n",
+      "loss: 0.576301  [  132/  240]\n",
+      "Epoch 562\n",
+      "-------------------------------\n",
+      "loss: 0.580891  [   12/  240]\n",
+      "loss: 0.593648  [  132/  240]\n",
+      "Epoch 563\n",
+      "-------------------------------\n",
+      "loss: 0.573099  [   12/  240]\n",
+      "loss: 0.588663  [  132/  240]\n",
+      "Epoch 564\n",
+      "-------------------------------\n",
+      "loss: 0.587425  [   12/  240]\n",
+      "loss: 0.581722  [  132/  240]\n",
+      "Epoch 565\n",
+      "-------------------------------\n",
+      "loss: 0.557625  [   12/  240]\n",
+      "loss: 0.590129  [  132/  240]\n",
+      "Epoch 566\n",
+      "-------------------------------\n",
+      "loss: 0.586836  [   12/  240]\n",
+      "loss: 0.560395  [  132/  240]\n",
+      "Epoch 567\n",
+      "-------------------------------\n",
+      "loss: 0.584912  [   12/  240]\n",
+      "loss: 0.595770  [  132/  240]\n",
+      "Epoch 568\n",
+      "-------------------------------\n",
+      "loss: 0.603508  [   12/  240]\n",
+      "loss: 0.592561  [  132/  240]\n",
+      "Epoch 569\n",
+      "-------------------------------\n",
+      "loss: 0.582687  [   12/  240]\n",
+      "loss: 0.598879  [  132/  240]\n",
+      "Epoch 570\n",
+      "-------------------------------\n",
+      "loss: 0.600297  [   12/  240]\n",
+      "loss: 0.559394  [  132/  240]\n",
+      "Epoch 571\n",
+      "-------------------------------\n",
+      "loss: 0.571853  [   12/  240]\n",
+      "loss: 24.734072  [  132/  240]\n",
+      "Epoch 572\n",
+      "-------------------------------\n",
+      "loss: 0.564443  [   12/  240]\n",
+      "loss: 0.590628  [  132/  240]\n",
+      "Epoch 573\n",
+      "-------------------------------\n",
+      "loss: 0.581794  [   12/  240]\n",
+      "loss: 0.578397  [  132/  240]\n",
+      "Epoch 574\n",
+      "-------------------------------\n",
+      "loss: 0.562765  [   12/  240]\n",
+      "loss: 0.571754  [  132/  240]\n",
+      "Epoch 575\n",
+      "-------------------------------\n",
+      "loss: 0.590283  [   12/  240]\n",
+      "loss: 18.899782  [  132/  240]\n",
+      "Epoch 576\n",
+      "-------------------------------\n",
+      "loss: 0.578380  [   12/  240]\n",
+      "loss: 0.594103  [  132/  240]\n",
+      "Epoch 577\n",
+      "-------------------------------\n",
+      "loss: 0.576605  [   12/  240]\n",
+      "loss: 0.587344  [  132/  240]\n",
+      "Epoch 578\n",
+      "-------------------------------\n",
+      "loss: 0.588446  [   12/  240]\n",
+      "loss: 0.574345  [  132/  240]\n",
+      "Epoch 579\n",
+      "-------------------------------\n",
+      "loss: 0.568193  [   12/  240]\n",
+      "loss: 0.557424  [  132/  240]\n",
+      "Epoch 580\n",
+      "-------------------------------\n",
+      "loss: 0.601650  [   12/  240]\n",
+      "loss: 0.597913  [  132/  240]\n",
+      "Epoch 581\n",
+      "-------------------------------\n",
+      "loss: 0.569653  [   12/  240]\n",
+      "loss: 1.271598  [  132/  240]\n",
+      "Epoch 582\n",
+      "-------------------------------\n",
+      "loss: 0.590643  [   12/  240]\n",
+      "loss: 0.571026  [  132/  240]\n",
+      "Epoch 583\n",
+      "-------------------------------\n",
+      "loss: 0.562598  [   12/  240]\n",
+      "loss: 0.580161  [  132/  240]\n",
+      "Epoch 584\n",
+      "-------------------------------\n",
+      "loss: 0.569771  [   12/  240]\n",
+      "loss: 0.579104  [  132/  240]\n",
+      "Epoch 585\n",
+      "-------------------------------\n",
+      "loss: 18.914200  [   12/  240]\n",
+      "loss: 0.594337  [  132/  240]\n",
+      "Epoch 586\n",
+      "-------------------------------\n",
+      "loss: 0.600810  [   12/  240]\n",
+      "loss: 0.568798  [  132/  240]\n",
+      "Epoch 587\n",
+      "-------------------------------\n",
+      "loss: 0.550426  [   12/  240]\n",
+      "loss: 0.568977  [  132/  240]\n",
+      "Epoch 588\n",
+      "-------------------------------\n",
+      "loss: 0.569199  [   12/  240]\n",
+      "loss: 0.572276  [  132/  240]\n",
+      "Epoch 589\n",
+      "-------------------------------\n",
+      "loss: 0.575120  [   12/  240]\n",
+      "loss: 24.737795  [  132/  240]\n",
+      "Epoch 590\n",
+      "-------------------------------\n",
+      "loss: 0.580122  [   12/  240]\n",
+      "loss: 0.577725  [  132/  240]\n",
+      "Epoch 591\n",
+      "-------------------------------\n",
+      "loss: 0.568156  [   12/  240]\n",
+      "loss: 0.583927  [  132/  240]\n",
+      "Epoch 592\n",
+      "-------------------------------\n",
+      "loss: 0.581244  [   12/  240]\n",
+      "loss: 0.601159  [  132/  240]\n",
+      "Epoch 593\n",
+      "-------------------------------\n",
+      "loss: 1.098135  [   12/  240]\n",
+      "loss: 0.614347  [  132/  240]\n",
+      "Epoch 594\n",
+      "-------------------------------\n",
+      "loss: 0.565321  [   12/  240]\n",
+      "loss: 24.707157  [  132/  240]\n",
+      "Epoch 595\n",
+      "-------------------------------\n",
+      "loss: 0.604265  [   12/  240]\n",
+      "loss: 0.565513  [  132/  240]\n",
+      "Epoch 596\n",
+      "-------------------------------\n",
+      "loss: 0.580175  [   12/  240]\n",
+      "loss: 0.596044  [  132/  240]\n",
+      "Epoch 597\n",
+      "-------------------------------\n",
+      "loss: 0.557101  [   12/  240]\n",
+      "loss: 0.570550  [  132/  240]\n",
+      "Epoch 598\n",
+      "-------------------------------\n",
+      "loss: 0.565746  [   12/  240]\n",
+      "loss: 0.552536  [  132/  240]\n",
+      "Epoch 599\n",
+      "-------------------------------\n",
+      "loss: 0.573196  [   12/  240]\n",
+      "loss: 0.592941  [  132/  240]\n",
+      "Epoch 600\n",
+      "-------------------------------\n",
+      "loss: 18.907290  [   12/  240]\n",
+      "loss: 0.584397  [  132/  240]\n",
+      "Epoch 601\n",
+      "-------------------------------\n",
+      "loss: 0.579353  [   12/  240]\n",
+      "loss: 0.596862  [  132/  240]\n",
+      "Epoch 602\n",
+      "-------------------------------\n",
+      "loss: 0.594270  [   12/  240]\n",
+      "loss: 0.596928  [  132/  240]\n",
+      "Epoch 603\n",
+      "-------------------------------\n",
+      "loss: 0.583429  [   12/  240]\n",
+      "loss: 0.585273  [  132/  240]\n",
+      "Epoch 604\n",
+      "-------------------------------\n",
+      "loss: 0.547419  [   12/  240]\n",
+      "loss: 0.559690  [  132/  240]\n",
+      "Epoch 605\n",
+      "-------------------------------\n",
+      "loss: 0.587566  [   12/  240]\n",
+      "loss: 0.577375  [  132/  240]\n",
+      "Epoch 606\n",
+      "-------------------------------\n",
+      "loss: 0.577319  [   12/  240]\n",
+      "loss: 0.574648  [  132/  240]\n",
+      "Epoch 607\n",
+      "-------------------------------\n",
+      "loss: 0.584125  [   12/  240]\n",
+      "loss: 0.570223  [  132/  240]\n",
+      "Epoch 608\n",
+      "-------------------------------\n",
+      "loss: 0.573130  [   12/  240]\n",
+      "loss: 0.568683  [  132/  240]\n",
+      "Epoch 609\n",
+      "-------------------------------\n",
+      "loss: 0.556500  [   12/  240]\n",
+      "loss: 0.569536  [  132/  240]\n",
+      "Epoch 610\n",
+      "-------------------------------\n",
+      "loss: 18.932590  [   12/  240]\n",
+      "loss: 0.565229  [  132/  240]\n",
+      "Epoch 611\n",
+      "-------------------------------\n",
+      "loss: 18.905663  [   12/  240]\n",
+      "loss: 0.569539  [  132/  240]\n",
+      "Epoch 612\n",
+      "-------------------------------\n",
+      "loss: 0.577512  [   12/  240]\n",
+      "loss: 0.580662  [  132/  240]\n",
+      "Epoch 613\n",
+      "-------------------------------\n",
+      "loss: 1.141710  [   12/  240]\n",
+      "loss: 0.586220  [  132/  240]\n",
+      "Epoch 614\n",
+      "-------------------------------\n",
+      "loss: 18.904703  [   12/  240]\n",
+      "loss: 0.588199  [  132/  240]\n",
+      "Epoch 615\n",
+      "-------------------------------\n",
+      "loss: 0.577225  [   12/  240]\n",
+      "loss: 18.912092  [  132/  240]\n",
+      "Epoch 616\n",
+      "-------------------------------\n",
+      "loss: 0.554176  [   12/  240]\n",
+      "loss: 0.593954  [  132/  240]\n",
+      "Epoch 617\n",
+      "-------------------------------\n",
+      "loss: 0.569799  [   12/  240]\n",
+      "loss: 0.580805  [  132/  240]\n",
+      "Epoch 618\n",
+      "-------------------------------\n",
+      "loss: 0.546154  [   12/  240]\n",
+      "loss: 0.581447  [  132/  240]\n",
+      "Epoch 619\n",
+      "-------------------------------\n",
+      "loss: 0.558134  [   12/  240]\n",
+      "loss: 0.569999  [  132/  240]\n",
+      "Epoch 620\n",
+      "-------------------------------\n",
+      "loss: 0.597118  [   12/  240]\n",
+      "loss: 0.565836  [  132/  240]\n",
+      "Epoch 621\n",
+      "-------------------------------\n",
+      "loss: 0.589888  [   12/  240]\n",
+      "loss: 0.576212  [  132/  240]\n",
+      "Epoch 622\n",
+      "-------------------------------\n",
+      "loss: 0.572414  [   12/  240]\n",
+      "loss: 0.579978  [  132/  240]\n",
+      "Epoch 623\n",
+      "-------------------------------\n",
+      "loss: 0.577739  [   12/  240]\n",
+      "loss: 0.588166  [  132/  240]\n",
+      "Epoch 624\n",
+      "-------------------------------\n",
+      "loss: 24.728165  [   12/  240]\n",
+      "loss: 0.575427  [  132/  240]\n",
+      "Epoch 625\n",
+      "-------------------------------\n",
+      "loss: 0.576006  [   12/  240]\n",
+      "loss: 0.577940  [  132/  240]\n",
+      "Epoch 626\n",
+      "-------------------------------\n",
+      "loss: 0.570812  [   12/  240]\n",
+      "loss: 0.582543  [  132/  240]\n",
+      "Epoch 627\n",
+      "-------------------------------\n",
+      "loss: 0.557973  [   12/  240]\n",
+      "loss: 0.564974  [  132/  240]\n",
+      "Epoch 628\n",
+      "-------------------------------\n",
+      "loss: 0.570543  [   12/  240]\n",
+      "loss: 0.568018  [  132/  240]\n",
+      "Epoch 629\n",
+      "-------------------------------\n",
+      "loss: 24.689516  [   12/  240]\n",
+      "loss: 0.583308  [  132/  240]\n",
+      "Epoch 630\n",
+      "-------------------------------\n",
+      "loss: 0.560929  [   12/  240]\n",
+      "loss: 0.578971  [  132/  240]\n",
+      "Epoch 631\n",
+      "-------------------------------\n",
+      "loss: 1.181086  [   12/  240]\n",
+      "loss: 0.585044  [  132/  240]\n",
+      "Epoch 632\n",
+      "-------------------------------\n",
+      "loss: 1.130602  [   12/  240]\n",
+      "loss: 0.591703  [  132/  240]\n",
+      "Epoch 633\n",
+      "-------------------------------\n",
+      "loss: 0.583590  [   12/  240]\n",
+      "loss: 0.573212  [  132/  240]\n",
+      "Epoch 634\n",
+      "-------------------------------\n",
+      "loss: 0.569765  [   12/  240]\n",
+      "loss: 0.562824  [  132/  240]\n",
+      "Epoch 635\n",
+      "-------------------------------\n",
+      "loss: 0.584771  [   12/  240]\n",
+      "loss: 0.575864  [  132/  240]\n",
+      "Epoch 636\n",
+      "-------------------------------\n",
+      "loss: 18.892595  [   12/  240]\n",
+      "loss: 0.566095  [  132/  240]\n",
+      "Epoch 637\n",
+      "-------------------------------\n",
+      "loss: 0.554911  [   12/  240]\n",
+      "loss: 1.229803  [  132/  240]\n",
+      "Epoch 638\n",
+      "-------------------------------\n",
+      "loss: 24.727461  [   12/  240]\n",
+      "loss: 0.561679  [  132/  240]\n",
+      "Epoch 639\n",
+      "-------------------------------\n",
+      "loss: 18.929436  [   12/  240]\n",
+      "loss: 0.561445  [  132/  240]\n",
+      "Epoch 640\n",
+      "-------------------------------\n",
+      "loss: 0.576307  [   12/  240]\n",
+      "loss: 0.560070  [  132/  240]\n",
+      "Epoch 641\n",
+      "-------------------------------\n",
+      "loss: 0.559376  [   12/  240]\n",
+      "loss: 0.573916  [  132/  240]\n",
+      "Epoch 642\n",
+      "-------------------------------\n",
+      "loss: 0.565530  [   12/  240]\n",
+      "loss: 0.558603  [  132/  240]\n",
+      "Epoch 643\n",
+      "-------------------------------\n",
+      "loss: 24.722250  [   12/  240]\n",
+      "loss: 0.576778  [  132/  240]\n",
+      "Epoch 644\n",
+      "-------------------------------\n",
+      "loss: 0.553810  [   12/  240]\n",
+      "loss: 0.587527  [  132/  240]\n",
+      "Epoch 645\n",
+      "-------------------------------\n",
+      "loss: 0.571499  [   12/  240]\n",
+      "loss: 0.564790  [  132/  240]\n",
+      "Epoch 646\n",
+      "-------------------------------\n",
+      "loss: 0.562479  [   12/  240]\n",
+      "loss: 0.592525  [  132/  240]\n",
+      "Epoch 647\n",
+      "-------------------------------\n",
+      "loss: 0.562817  [   12/  240]\n",
+      "loss: 0.552837  [  132/  240]\n",
+      "Epoch 648\n",
+      "-------------------------------\n",
+      "loss: 0.563056  [   12/  240]\n",
+      "loss: 0.567744  [  132/  240]\n",
+      "Epoch 649\n",
+      "-------------------------------\n",
+      "loss: 25.348129  [   12/  240]\n",
+      "loss: 18.931953  [  132/  240]\n",
+      "Epoch 650\n",
+      "-------------------------------\n",
+      "loss: 0.579511  [   12/  240]\n",
+      "loss: 0.585844  [  132/  240]\n",
+      "Epoch 651\n",
+      "-------------------------------\n",
+      "loss: 0.566940  [   12/  240]\n",
+      "loss: 0.555511  [  132/  240]\n",
+      "Epoch 652\n",
+      "-------------------------------\n",
+      "loss: 0.551451  [   12/  240]\n",
+      "loss: 0.576584  [  132/  240]\n",
+      "Epoch 653\n",
+      "-------------------------------\n",
+      "loss: 0.560343  [   12/  240]\n",
+      "loss: 0.537012  [  132/  240]\n",
+      "Epoch 654\n",
+      "-------------------------------\n",
+      "loss: 0.555458  [   12/  240]\n",
+      "loss: 0.568878  [  132/  240]\n",
+      "Epoch 655\n",
+      "-------------------------------\n",
+      "loss: 0.562537  [   12/  240]\n",
+      "loss: 24.746555  [  132/  240]\n",
+      "Epoch 656\n",
+      "-------------------------------\n",
+      "loss: 0.567938  [   12/  240]\n",
+      "loss: 0.597853  [  132/  240]\n",
+      "Epoch 657\n",
+      "-------------------------------\n",
+      "loss: 18.898016  [   12/  240]\n",
+      "loss: 0.565171  [  132/  240]\n",
+      "Epoch 658\n",
+      "-------------------------------\n",
+      "loss: 0.575458  [   12/  240]\n",
+      "loss: 0.577992  [  132/  240]\n",
+      "Epoch 659\n",
+      "-------------------------------\n",
+      "loss: 0.577560  [   12/  240]\n",
+      "loss: 0.568328  [  132/  240]\n",
+      "Epoch 660\n",
+      "-------------------------------\n",
+      "loss: 0.560641  [   12/  240]\n",
+      "loss: 24.702686  [  132/  240]\n",
+      "Epoch 661\n",
+      "-------------------------------\n",
+      "loss: 0.582740  [   12/  240]\n",
+      "loss: 0.606339  [  132/  240]\n",
+      "Epoch 662\n",
+      "-------------------------------\n",
+      "loss: 0.574713  [   12/  240]\n",
+      "loss: 0.564649  [  132/  240]\n",
+      "Epoch 663\n",
+      "-------------------------------\n",
+      "loss: 24.720932  [   12/  240]\n",
+      "loss: 0.560359  [  132/  240]\n",
+      "Epoch 664\n",
+      "-------------------------------\n",
+      "loss: 0.558694  [   12/  240]\n",
+      "loss: 0.569716  [  132/  240]\n",
+      "Epoch 665\n",
+      "-------------------------------\n",
+      "loss: 0.581442  [   12/  240]\n",
+      "loss: 0.581017  [  132/  240]\n",
+      "Epoch 666\n",
+      "-------------------------------\n",
+      "loss: 0.588884  [   12/  240]\n",
+      "loss: 0.578882  [  132/  240]\n",
+      "Epoch 667\n",
+      "-------------------------------\n",
+      "loss: 0.575757  [   12/  240]\n",
+      "loss: 0.545613  [  132/  240]\n",
+      "Epoch 668\n",
+      "-------------------------------\n",
+      "loss: 0.559816  [   12/  240]\n",
+      "loss: 0.540088  [  132/  240]\n",
+      "Epoch 669\n",
+      "-------------------------------\n",
+      "loss: 0.586613  [   12/  240]\n",
+      "loss: 0.567348  [  132/  240]\n",
+      "Epoch 670\n",
+      "-------------------------------\n",
+      "loss: 0.570909  [   12/  240]\n",
+      "loss: 0.576247  [  132/  240]\n",
+      "Epoch 671\n",
+      "-------------------------------\n",
+      "loss: 0.546061  [   12/  240]\n",
+      "loss: 0.560390  [  132/  240]\n",
+      "Epoch 672\n",
+      "-------------------------------\n",
+      "loss: 1.091891  [   12/  240]\n",
+      "loss: 0.574440  [  132/  240]\n",
+      "Epoch 673\n",
+      "-------------------------------\n",
+      "loss: 0.563669  [   12/  240]\n",
+      "loss: 0.562030  [  132/  240]\n",
+      "Epoch 674\n",
+      "-------------------------------\n",
+      "loss: 0.582477  [   12/  240]\n",
+      "loss: 0.558678  [  132/  240]\n",
+      "Epoch 675\n",
+      "-------------------------------\n",
+      "loss: 0.575828  [   12/  240]\n",
+      "loss: 0.586835  [  132/  240]\n",
+      "Epoch 676\n",
+      "-------------------------------\n",
+      "loss: 0.577066  [   12/  240]\n",
+      "loss: 0.542906  [  132/  240]\n",
+      "Epoch 677\n",
+      "-------------------------------\n",
+      "loss: 0.568986  [   12/  240]\n",
+      "loss: 0.552221  [  132/  240]\n",
+      "Epoch 678\n",
+      "-------------------------------\n",
+      "loss: 0.569057  [   12/  240]\n",
+      "loss: 0.547127  [  132/  240]\n",
+      "Epoch 679\n",
+      "-------------------------------\n",
+      "loss: 0.581681  [   12/  240]\n",
+      "loss: 0.558158  [  132/  240]\n",
+      "Epoch 680\n",
+      "-------------------------------\n",
+      "loss: 18.905460  [   12/  240]\n",
+      "loss: 1.265728  [  132/  240]\n",
+      "Epoch 681\n",
+      "-------------------------------\n",
+      "loss: 0.568441  [   12/  240]\n",
+      "loss: 0.556171  [  132/  240]\n",
+      "Epoch 682\n",
+      "-------------------------------\n",
+      "loss: 0.565034  [   12/  240]\n",
+      "loss: 0.550458  [  132/  240]\n",
+      "Epoch 683\n",
+      "-------------------------------\n",
+      "loss: 0.557434  [   12/  240]\n",
+      "loss: 0.569059  [  132/  240]\n",
+      "Epoch 684\n",
+      "-------------------------------\n",
+      "loss: 0.550453  [   12/  240]\n",
+      "loss: 0.572802  [  132/  240]\n",
+      "Epoch 685\n",
+      "-------------------------------\n",
+      "loss: 24.730745  [   12/  240]\n",
+      "loss: 0.568695  [  132/  240]\n",
+      "Epoch 686\n",
+      "-------------------------------\n",
+      "loss: 0.572061  [   12/  240]\n",
+      "loss: 0.559034  [  132/  240]\n",
+      "Epoch 687\n",
+      "-------------------------------\n",
+      "loss: 0.550528  [   12/  240]\n",
+      "loss: 0.550679  [  132/  240]\n",
+      "Epoch 688\n",
+      "-------------------------------\n",
+      "loss: 0.567952  [   12/  240]\n",
+      "loss: 0.578179  [  132/  240]\n",
+      "Epoch 689\n",
+      "-------------------------------\n",
+      "loss: 0.551909  [   12/  240]\n",
+      "loss: 24.706959  [  132/  240]\n",
+      "Epoch 690\n",
+      "-------------------------------\n",
+      "loss: 0.565932  [   12/  240]\n",
+      "loss: 0.582085  [  132/  240]\n",
+      "Epoch 691\n",
+      "-------------------------------\n",
+      "loss: 0.582724  [   12/  240]\n",
+      "loss: 0.540045  [  132/  240]\n",
+      "Epoch 692\n",
+      "-------------------------------\n",
+      "loss: 0.570212  [   12/  240]\n",
+      "loss: 0.556590  [  132/  240]\n",
+      "Epoch 693\n",
+      "-------------------------------\n",
+      "loss: 1.084144  [   12/  240]\n",
+      "loss: 0.591316  [  132/  240]\n",
+      "Epoch 694\n",
+      "-------------------------------\n",
+      "loss: 0.585218  [   12/  240]\n",
+      "loss: 0.566774  [  132/  240]\n",
+      "Epoch 695\n",
+      "-------------------------------\n",
+      "loss: 0.546160  [   12/  240]\n",
+      "loss: 0.570270  [  132/  240]\n",
+      "Epoch 696\n",
+      "-------------------------------\n",
+      "loss: 0.565857  [   12/  240]\n",
+      "loss: 0.585199  [  132/  240]\n",
+      "Epoch 697\n",
+      "-------------------------------\n",
+      "loss: 0.577650  [   12/  240]\n",
+      "loss: 0.552638  [  132/  240]\n",
+      "Epoch 698\n",
+      "-------------------------------\n",
+      "loss: 18.927120  [   12/  240]\n",
+      "loss: 0.556825  [  132/  240]\n",
+      "Epoch 699\n",
+      "-------------------------------\n",
+      "loss: 0.594408  [   12/  240]\n",
+      "loss: 0.563390  [  132/  240]\n",
+      "Epoch 700\n",
+      "-------------------------------\n",
+      "loss: 0.570915  [   12/  240]\n",
+      "loss: 0.566092  [  132/  240]\n",
+      "Epoch 701\n",
+      "-------------------------------\n",
+      "loss: 0.555827  [   12/  240]\n",
+      "loss: 0.560171  [  132/  240]\n",
+      "Epoch 702\n",
+      "-------------------------------\n",
+      "loss: 0.552019  [   12/  240]\n",
+      "loss: 0.579320  [  132/  240]\n",
+      "Epoch 703\n",
+      "-------------------------------\n",
+      "loss: 0.551761  [   12/  240]\n",
+      "loss: 1.269159  [  132/  240]\n",
+      "Epoch 704\n",
+      "-------------------------------\n",
+      "loss: 0.568778  [   12/  240]\n",
+      "loss: 0.567194  [  132/  240]\n",
+      "Epoch 705\n",
+      "-------------------------------\n",
+      "loss: 0.571358  [   12/  240]\n",
+      "loss: 0.595384  [  132/  240]\n",
+      "Epoch 706\n",
+      "-------------------------------\n",
+      "loss: 0.593001  [   12/  240]\n",
+      "loss: 0.571670  [  132/  240]\n",
+      "Epoch 707\n",
+      "-------------------------------\n",
+      "loss: 0.544277  [   12/  240]\n",
+      "loss: 1.249660  [  132/  240]\n",
+      "Epoch 708\n",
+      "-------------------------------\n",
+      "loss: 0.547343  [   12/  240]\n",
+      "loss: 0.541800  [  132/  240]\n",
+      "Epoch 709\n",
+      "-------------------------------\n",
+      "loss: 0.559512  [   12/  240]\n",
+      "loss: 0.551103  [  132/  240]\n",
+      "Epoch 710\n",
+      "-------------------------------\n",
+      "loss: 0.556469  [   12/  240]\n",
+      "loss: 0.558513  [  132/  240]\n",
+      "Epoch 711\n",
+      "-------------------------------\n",
+      "loss: 0.576900  [   12/  240]\n",
+      "loss: 0.553692  [  132/  240]\n",
+      "Epoch 712\n",
+      "-------------------------------\n",
+      "loss: 0.583177  [   12/  240]\n",
+      "loss: 0.564744  [  132/  240]\n",
+      "Epoch 713\n",
+      "-------------------------------\n",
+      "loss: 0.552618  [   12/  240]\n",
+      "loss: 0.541213  [  132/  240]\n",
+      "Epoch 714\n",
+      "-------------------------------\n",
+      "loss: 0.584241  [   12/  240]\n",
+      "loss: 0.573238  [  132/  240]\n",
+      "Epoch 715\n",
+      "-------------------------------\n",
+      "loss: 0.579427  [   12/  240]\n",
+      "loss: 24.717400  [  132/  240]\n",
+      "Epoch 716\n",
+      "-------------------------------\n",
+      "loss: 0.574615  [   12/  240]\n",
+      "loss: 0.555115  [  132/  240]\n",
+      "Epoch 717\n",
+      "-------------------------------\n",
+      "loss: 0.564917  [   12/  240]\n",
+      "loss: 0.581747  [  132/  240]\n",
+      "Epoch 718\n",
+      "-------------------------------\n",
+      "loss: 0.570490  [   12/  240]\n",
+      "loss: 0.579036  [  132/  240]\n",
+      "Epoch 719\n",
+      "-------------------------------\n",
+      "loss: 0.584603  [   12/  240]\n",
+      "loss: 0.553341  [  132/  240]\n",
+      "Epoch 720\n",
+      "-------------------------------\n",
+      "loss: 18.930408  [   12/  240]\n",
+      "loss: 0.566425  [  132/  240]\n",
+      "Epoch 721\n",
+      "-------------------------------\n",
+      "loss: 19.579575  [   12/  240]\n",
+      "loss: 0.585999  [  132/  240]\n",
+      "Epoch 722\n",
+      "-------------------------------\n",
+      "loss: 0.550343  [   12/  240]\n",
+      "loss: 0.570464  [  132/  240]\n",
+      "Epoch 723\n",
+      "-------------------------------\n",
+      "loss: 24.729900  [   12/  240]\n",
+      "loss: 0.561051  [  132/  240]\n",
+      "Epoch 724\n",
+      "-------------------------------\n",
+      "loss: 0.552262  [   12/  240]\n",
+      "loss: 0.574937  [  132/  240]\n",
+      "Epoch 725\n",
+      "-------------------------------\n",
+      "loss: 0.585913  [   12/  240]\n",
+      "loss: 0.555393  [  132/  240]\n",
+      "Epoch 726\n",
+      "-------------------------------\n",
+      "loss: 1.205724  [   12/  240]\n",
+      "loss: 0.583645  [  132/  240]\n",
+      "Epoch 727\n",
+      "-------------------------------\n",
+      "loss: 0.568631  [   12/  240]\n",
+      "loss: 0.585606  [  132/  240]\n",
+      "Epoch 728\n",
+      "-------------------------------\n",
+      "loss: 0.563034  [   12/  240]\n",
+      "loss: 0.563009  [  132/  240]\n",
+      "Epoch 729\n",
+      "-------------------------------\n",
+      "loss: 0.575459  [   12/  240]\n",
+      "loss: 18.890804  [  132/  240]\n",
+      "Epoch 730\n",
+      "-------------------------------\n",
+      "loss: 0.546260  [   12/  240]\n",
+      "loss: 0.561692  [  132/  240]\n",
+      "Epoch 731\n",
+      "-------------------------------\n",
+      "loss: 0.569133  [   12/  240]\n",
+      "loss: 0.551987  [  132/  240]\n",
+      "Epoch 732\n",
+      "-------------------------------\n",
+      "loss: 0.588049  [   12/  240]\n",
+      "loss: 0.585768  [  132/  240]\n",
+      "Epoch 733\n",
+      "-------------------------------\n",
+      "loss: 1.089714  [   12/  240]\n",
+      "loss: 0.578641  [  132/  240]\n",
+      "Epoch 734\n",
+      "-------------------------------\n",
+      "loss: 0.579159  [   12/  240]\n",
+      "loss: 0.558498  [  132/  240]\n",
+      "Epoch 735\n",
+      "-------------------------------\n",
+      "loss: 0.552221  [   12/  240]\n",
+      "loss: 0.560451  [  132/  240]\n",
+      "Epoch 736\n",
+      "-------------------------------\n",
+      "loss: 0.550856  [   12/  240]\n",
+      "loss: 18.929943  [  132/  240]\n",
+      "Epoch 737\n",
+      "-------------------------------\n",
+      "loss: 18.921671  [   12/  240]\n",
+      "loss: 0.575285  [  132/  240]\n",
+      "Epoch 738\n",
+      "-------------------------------\n",
+      "loss: 0.567406  [   12/  240]\n",
+      "loss: 0.557140  [  132/  240]\n",
+      "Epoch 739\n",
+      "-------------------------------\n",
+      "loss: 0.571533  [   12/  240]\n",
+      "loss: 0.555671  [  132/  240]\n",
+      "Epoch 740\n",
+      "-------------------------------\n",
+      "loss: 0.559194  [   12/  240]\n",
+      "loss: 0.575897  [  132/  240]\n",
+      "Epoch 741\n",
+      "-------------------------------\n",
+      "loss: 0.571414  [   12/  240]\n",
+      "loss: 1.256567  [  132/  240]\n",
+      "Epoch 742\n",
+      "-------------------------------\n",
+      "loss: 0.549411  [   12/  240]\n",
+      "loss: 24.715836  [  132/  240]\n",
+      "Epoch 743\n",
+      "-------------------------------\n",
+      "loss: 0.564611  [   12/  240]\n",
+      "loss: 0.558725  [  132/  240]\n",
+      "Epoch 744\n",
+      "-------------------------------\n",
+      "loss: 0.571570  [   12/  240]\n",
+      "loss: 1.239358  [  132/  240]\n",
+      "Epoch 745\n",
+      "-------------------------------\n",
+      "loss: 25.262680  [   12/  240]\n",
+      "loss: 18.938166  [  132/  240]\n",
+      "Epoch 746\n",
+      "-------------------------------\n",
+      "loss: 0.571377  [   12/  240]\n",
+      "loss: 0.590246  [  132/  240]\n",
+      "Epoch 747\n",
+      "-------------------------------\n",
+      "loss: 0.540856  [   12/  240]\n",
+      "loss: 18.924759  [  132/  240]\n",
+      "Epoch 748\n",
+      "-------------------------------\n",
+      "loss: 0.545216  [   12/  240]\n",
+      "loss: 0.582439  [  132/  240]\n",
+      "Epoch 749\n",
+      "-------------------------------\n",
+      "loss: 0.555055  [   12/  240]\n",
+      "loss: 0.568477  [  132/  240]\n",
+      "Epoch 750\n",
+      "-------------------------------\n",
+      "loss: 18.924023  [   12/  240]\n",
+      "loss: 0.561478  [  132/  240]\n",
+      "Epoch 751\n",
+      "-------------------------------\n",
+      "loss: 0.563957  [   12/  240]\n",
+      "loss: 0.573884  [  132/  240]\n",
+      "Epoch 752\n",
+      "-------------------------------\n",
+      "loss: 0.576717  [   12/  240]\n",
+      "loss: 18.944361  [  132/  240]\n",
+      "Epoch 753\n",
+      "-------------------------------\n",
+      "loss: 0.568399  [   12/  240]\n",
+      "loss: 0.550630  [  132/  240]\n",
+      "Epoch 754\n",
+      "-------------------------------\n",
+      "loss: 0.566951  [   12/  240]\n",
+      "loss: 24.724916  [  132/  240]\n",
+      "Epoch 755\n",
+      "-------------------------------\n",
+      "loss: 0.554631  [   12/  240]\n",
+      "loss: 0.548230  [  132/  240]\n",
+      "Epoch 756\n",
+      "-------------------------------\n",
+      "loss: 0.582709  [   12/  240]\n",
+      "loss: 18.912142  [  132/  240]\n",
+      "Epoch 757\n",
+      "-------------------------------\n",
+      "loss: 0.565152  [   12/  240]\n",
+      "loss: 1.206376  [  132/  240]\n",
+      "Epoch 758\n",
+      "-------------------------------\n",
+      "loss: 0.583108  [   12/  240]\n",
+      "loss: 0.595286  [  132/  240]\n",
+      "Epoch 759\n",
+      "-------------------------------\n",
+      "loss: 0.555647  [   12/  240]\n",
+      "loss: 0.565214  [  132/  240]\n",
+      "Epoch 760\n",
+      "-------------------------------\n",
+      "loss: 0.572612  [   12/  240]\n",
+      "loss: 0.575233  [  132/  240]\n",
+      "Epoch 761\n",
+      "-------------------------------\n",
+      "loss: 0.545621  [   12/  240]\n",
+      "loss: 0.564238  [  132/  240]\n",
+      "Epoch 762\n",
+      "-------------------------------\n",
+      "loss: 0.550793  [   12/  240]\n",
+      "loss: 18.907194  [  132/  240]\n",
+      "Epoch 763\n",
+      "-------------------------------\n",
+      "loss: 0.561921  [   12/  240]\n",
+      "loss: 0.562952  [  132/  240]\n",
+      "Epoch 764\n",
+      "-------------------------------\n",
+      "loss: 0.541207  [   12/  240]\n",
+      "loss: 0.581965  [  132/  240]\n",
+      "Epoch 765\n",
+      "-------------------------------\n",
+      "loss: 0.572581  [   12/  240]\n",
+      "loss: 0.559628  [  132/  240]\n",
+      "Epoch 766\n",
+      "-------------------------------\n",
+      "loss: 0.555201  [   12/  240]\n",
+      "loss: 18.935860  [  132/  240]\n",
+      "Epoch 767\n",
+      "-------------------------------\n",
+      "loss: 0.561685  [   12/  240]\n",
+      "loss: 1.219159  [  132/  240]\n",
+      "Epoch 768\n",
+      "-------------------------------\n",
+      "loss: 0.580269  [   12/  240]\n",
+      "loss: 0.554185  [  132/  240]\n",
+      "Epoch 769\n",
+      "-------------------------------\n",
+      "loss: 0.581407  [   12/  240]\n",
+      "loss: 0.573705  [  132/  240]\n",
+      "Epoch 770\n",
+      "-------------------------------\n",
+      "loss: 0.559062  [   12/  240]\n",
+      "loss: 0.562547  [  132/  240]\n",
+      "Epoch 771\n",
+      "-------------------------------\n",
+      "loss: 0.602873  [   12/  240]\n",
+      "loss: 18.939077  [  132/  240]\n",
+      "Epoch 772\n",
+      "-------------------------------\n",
+      "loss: 0.558234  [   12/  240]\n",
+      "loss: 0.571985  [  132/  240]\n",
+      "Epoch 773\n",
+      "-------------------------------\n",
+      "loss: 0.567422  [   12/  240]\n",
+      "loss: 0.560834  [  132/  240]\n",
+      "Epoch 774\n",
+      "-------------------------------\n",
+      "loss: 0.561553  [   12/  240]\n",
+      "loss: 0.560059  [  132/  240]\n",
+      "Epoch 775\n",
+      "-------------------------------\n",
+      "loss: 0.583644  [   12/  240]\n",
+      "loss: 0.538817  [  132/  240]\n",
+      "Epoch 776\n",
+      "-------------------------------\n",
+      "loss: 0.565050  [   12/  240]\n",
+      "loss: 1.230889  [  132/  240]\n",
+      "Epoch 777\n",
+      "-------------------------------\n",
+      "loss: 0.569355  [   12/  240]\n",
+      "loss: 0.590750  [  132/  240]\n",
+      "Epoch 778\n",
+      "-------------------------------\n",
+      "loss: 0.544067  [   12/  240]\n",
+      "loss: 0.585247  [  132/  240]\n",
+      "Epoch 779\n",
+      "-------------------------------\n",
+      "loss: 0.533922  [   12/  240]\n",
+      "loss: 0.551466  [  132/  240]\n",
+      "Epoch 780\n",
+      "-------------------------------\n",
+      "loss: 0.568444  [   12/  240]\n",
+      "loss: 0.573285  [  132/  240]\n",
+      "Epoch 781\n",
+      "-------------------------------\n",
+      "loss: 0.553527  [   12/  240]\n",
+      "loss: 0.593989  [  132/  240]\n",
+      "Epoch 782\n",
+      "-------------------------------\n",
+      "loss: 0.577051  [   12/  240]\n",
+      "loss: 0.571503  [  132/  240]\n",
+      "Epoch 783\n",
+      "-------------------------------\n",
+      "loss: 0.567498  [   12/  240]\n",
+      "loss: 24.714556  [  132/  240]\n",
+      "Epoch 784\n",
+      "-------------------------------\n",
+      "loss: 0.579641  [   12/  240]\n",
+      "loss: 24.714903  [  132/  240]\n",
+      "Epoch 785\n",
+      "-------------------------------\n",
+      "loss: 0.562190  [   12/  240]\n",
+      "loss: 0.547416  [  132/  240]\n",
+      "Epoch 786\n",
+      "-------------------------------\n",
+      "loss: 0.564934  [   12/  240]\n",
+      "loss: 0.548073  [  132/  240]\n",
+      "Epoch 787\n",
+      "-------------------------------\n",
+      "loss: 0.561568  [   12/  240]\n",
+      "loss: 0.544900  [  132/  240]\n",
+      "Epoch 788\n",
+      "-------------------------------\n",
+      "loss: 0.563404  [   12/  240]\n",
+      "loss: 0.569770  [  132/  240]\n",
+      "Epoch 789\n",
+      "-------------------------------\n",
+      "loss: 1.069566  [   12/  240]\n",
+      "loss: 18.917393  [  132/  240]\n",
+      "Epoch 790\n",
+      "-------------------------------\n",
+      "loss: 0.556439  [   12/  240]\n",
+      "loss: 0.585733  [  132/  240]\n",
+      "Epoch 791\n",
+      "-------------------------------\n",
+      "loss: 0.550147  [   12/  240]\n",
+      "loss: 0.572103  [  132/  240]\n",
+      "Epoch 792\n",
+      "-------------------------------\n",
+      "loss: 0.567002  [   12/  240]\n",
+      "loss: 0.540432  [  132/  240]\n",
+      "Epoch 793\n",
+      "-------------------------------\n",
+      "loss: 0.545810  [   12/  240]\n",
+      "loss: 0.546830  [  132/  240]\n",
+      "Epoch 794\n",
+      "-------------------------------\n",
+      "loss: 0.563622  [   12/  240]\n",
+      "loss: 0.556505  [  132/  240]\n",
+      "Epoch 795\n",
+      "-------------------------------\n",
+      "loss: 0.595525  [   12/  240]\n",
+      "loss: 0.578947  [  132/  240]\n",
+      "Epoch 796\n",
+      "-------------------------------\n",
+      "loss: 0.570274  [   12/  240]\n",
+      "loss: 0.573327  [  132/  240]\n",
+      "Epoch 797\n",
+      "-------------------------------\n",
+      "loss: 0.549694  [   12/  240]\n",
+      "loss: 0.565895  [  132/  240]\n",
+      "Epoch 798\n",
+      "-------------------------------\n",
+      "loss: 0.538293  [   12/  240]\n",
+      "loss: 0.588369  [  132/  240]\n",
+      "Epoch 799\n",
+      "-------------------------------\n",
+      "loss: 0.567323  [   12/  240]\n",
+      "loss: 18.942709  [  132/  240]\n",
+      "Epoch 800\n",
+      "-------------------------------\n",
+      "loss: 24.721464  [   12/  240]\n",
+      "loss: 0.549924  [  132/  240]\n",
+      "Epoch 801\n",
+      "-------------------------------\n",
+      "loss: 0.565208  [   12/  240]\n",
+      "loss: 0.575309  [  132/  240]\n",
+      "Epoch 802\n",
+      "-------------------------------\n",
+      "loss: 0.567463  [   12/  240]\n",
+      "loss: 0.568845  [  132/  240]\n",
+      "Epoch 803\n",
+      "-------------------------------\n",
+      "loss: 0.576372  [   12/  240]\n",
+      "loss: 0.555166  [  132/  240]\n",
+      "Epoch 804\n",
+      "-------------------------------\n",
+      "loss: 0.554783  [   12/  240]\n",
+      "loss: 0.544009  [  132/  240]\n",
+      "Epoch 805\n",
+      "-------------------------------\n",
+      "loss: 0.556744  [   12/  240]\n",
+      "loss: 0.566644  [  132/  240]\n",
+      "Epoch 806\n",
+      "-------------------------------\n",
+      "loss: 0.582241  [   12/  240]\n",
+      "loss: 0.563655  [  132/  240]\n",
+      "Epoch 807\n",
+      "-------------------------------\n",
+      "loss: 18.910885  [   12/  240]\n",
+      "loss: 0.563265  [  132/  240]\n",
+      "Epoch 808\n",
+      "-------------------------------\n",
+      "loss: 0.569306  [   12/  240]\n",
+      "loss: 0.558111  [  132/  240]\n",
+      "Epoch 809\n",
+      "-------------------------------\n",
+      "loss: 0.582575  [   12/  240]\n",
+      "loss: 0.550228  [  132/  240]\n",
+      "Epoch 810\n",
+      "-------------------------------\n",
+      "loss: 0.554071  [   12/  240]\n",
+      "loss: 0.566263  [  132/  240]\n",
+      "Epoch 811\n",
+      "-------------------------------\n",
+      "loss: 24.734806  [   12/  240]\n",
+      "loss: 0.567347  [  132/  240]\n",
+      "Epoch 812\n",
+      "-------------------------------\n",
+      "loss: 0.561871  [   12/  240]\n",
+      "loss: 0.582608  [  132/  240]\n",
+      "Epoch 813\n",
+      "-------------------------------\n",
+      "loss: 24.703348  [   12/  240]\n",
+      "loss: 0.557257  [  132/  240]\n",
+      "Epoch 814\n",
+      "-------------------------------\n",
+      "loss: 0.541556  [   12/  240]\n",
+      "loss: 0.572722  [  132/  240]\n",
+      "Epoch 815\n",
+      "-------------------------------\n",
+      "loss: 0.552996  [   12/  240]\n",
+      "loss: 0.565542  [  132/  240]\n",
+      "Epoch 816\n",
+      "-------------------------------\n",
+      "loss: 0.561039  [   12/  240]\n",
+      "loss: 0.556790  [  132/  240]\n",
+      "Epoch 817\n",
+      "-------------------------------\n",
+      "loss: 0.578336  [   12/  240]\n",
+      "loss: 0.551110  [  132/  240]\n",
+      "Epoch 818\n",
+      "-------------------------------\n",
+      "loss: 18.919077  [   12/  240]\n",
+      "loss: 0.552628  [  132/  240]\n",
+      "Epoch 819\n",
+      "-------------------------------\n",
+      "loss: 0.544132  [   12/  240]\n",
+      "loss: 0.555655  [  132/  240]\n",
+      "Epoch 820\n",
+      "-------------------------------\n",
+      "loss: 0.564618  [   12/  240]\n",
+      "loss: 1.246374  [  132/  240]\n",
+      "Epoch 821\n",
+      "-------------------------------\n",
+      "loss: 0.557042  [   12/  240]\n",
+      "loss: 0.568852  [  132/  240]\n",
+      "Epoch 822\n",
+      "-------------------------------\n",
+      "loss: 0.548675  [   12/  240]\n",
+      "loss: 0.560524  [  132/  240]\n",
+      "Epoch 823\n",
+      "-------------------------------\n",
+      "loss: 0.545750  [   12/  240]\n",
+      "loss: 0.545494  [  132/  240]\n",
+      "Epoch 824\n",
+      "-------------------------------\n",
+      "loss: 0.561113  [   12/  240]\n",
+      "loss: 0.562828  [  132/  240]\n",
+      "Epoch 825\n",
+      "-------------------------------\n",
+      "loss: 24.701162  [   12/  240]\n",
+      "loss: 0.551784  [  132/  240]\n",
+      "Epoch 826\n",
+      "-------------------------------\n",
+      "loss: 24.721859  [   12/  240]\n",
+      "loss: 0.550056  [  132/  240]\n",
+      "Epoch 827\n",
+      "-------------------------------\n",
+      "loss: 0.564971  [   12/  240]\n",
+      "loss: 24.733316  [  132/  240]\n",
+      "Epoch 828\n",
+      "-------------------------------\n",
+      "loss: 0.566007  [   12/  240]\n",
+      "loss: 0.559615  [  132/  240]\n",
+      "Epoch 829\n",
+      "-------------------------------\n",
+      "loss: 0.573084  [   12/  240]\n",
+      "loss: 0.573049  [  132/  240]\n",
+      "Epoch 830\n",
+      "-------------------------------\n",
+      "loss: 0.594989  [   12/  240]\n",
+      "loss: 0.555736  [  132/  240]\n",
+      "Epoch 831\n",
+      "-------------------------------\n",
+      "loss: 0.565884  [   12/  240]\n",
+      "loss: 0.561124  [  132/  240]\n",
+      "Epoch 832\n",
+      "-------------------------------\n",
+      "loss: 0.568777  [   12/  240]\n",
+      "loss: 0.546000  [  132/  240]\n",
+      "Epoch 833\n",
+      "-------------------------------\n",
+      "loss: 1.109406  [   12/  240]\n",
+      "loss: 0.561614  [  132/  240]\n",
+      "Epoch 834\n",
+      "-------------------------------\n",
+      "loss: 0.544460  [   12/  240]\n",
+      "loss: 0.581762  [  132/  240]\n",
+      "Epoch 835\n",
+      "-------------------------------\n",
+      "loss: 0.560655  [   12/  240]\n",
+      "loss: 0.567325  [  132/  240]\n",
+      "Epoch 836\n",
+      "-------------------------------\n",
+      "loss: 0.573575  [   12/  240]\n",
+      "loss: 0.569692  [  132/  240]\n",
+      "Epoch 837\n",
+      "-------------------------------\n",
+      "loss: 0.561217  [   12/  240]\n",
+      "loss: 0.561339  [  132/  240]\n",
+      "Epoch 838\n",
+      "-------------------------------\n",
+      "loss: 0.562902  [   12/  240]\n",
+      "loss: 0.564381  [  132/  240]\n",
+      "Epoch 839\n",
+      "-------------------------------\n",
+      "loss: 0.564342  [   12/  240]\n",
+      "loss: 0.540826  [  132/  240]\n",
+      "Epoch 840\n",
+      "-------------------------------\n",
+      "loss: 0.581362  [   12/  240]\n",
+      "loss: 0.558510  [  132/  240]\n",
+      "Epoch 841\n",
+      "-------------------------------\n",
+      "loss: 0.578689  [   12/  240]\n",
+      "loss: 0.564684  [  132/  240]\n",
+      "Epoch 842\n",
+      "-------------------------------\n",
+      "loss: 24.705830  [   12/  240]\n",
+      "loss: 0.554930  [  132/  240]\n",
+      "Epoch 843\n",
+      "-------------------------------\n",
+      "loss: 1.257515  [   12/  240]\n",
+      "loss: 0.590062  [  132/  240]\n",
+      "Epoch 844\n",
+      "-------------------------------\n",
+      "loss: 0.554028  [   12/  240]\n",
+      "loss: 0.575607  [  132/  240]\n",
+      "Epoch 845\n",
+      "-------------------------------\n",
+      "loss: 0.577733  [   12/  240]\n",
+      "loss: 0.572103  [  132/  240]\n",
+      "Epoch 846\n",
+      "-------------------------------\n",
+      "loss: 1.104159  [   12/  240]\n",
+      "loss: 0.578990  [  132/  240]\n",
+      "Epoch 847\n",
+      "-------------------------------\n",
+      "loss: 0.567597  [   12/  240]\n",
+      "loss: 0.562816  [  132/  240]\n",
+      "Epoch 848\n",
+      "-------------------------------\n",
+      "loss: 0.540809  [   12/  240]\n",
+      "loss: 0.556084  [  132/  240]\n",
+      "Epoch 849\n",
+      "-------------------------------\n",
+      "loss: 0.562340  [   12/  240]\n",
+      "loss: 0.553048  [  132/  240]\n",
+      "Epoch 850\n",
+      "-------------------------------\n",
+      "loss: 0.558311  [   12/  240]\n",
+      "loss: 0.548908  [  132/  240]\n",
+      "Epoch 851\n",
+      "-------------------------------\n",
+      "loss: 0.575908  [   12/  240]\n",
+      "loss: 0.570633  [  132/  240]\n",
+      "Epoch 852\n",
+      "-------------------------------\n",
+      "loss: 0.556119  [   12/  240]\n",
+      "loss: 0.548083  [  132/  240]\n",
+      "Epoch 853\n",
+      "-------------------------------\n",
+      "loss: 19.475977  [   12/  240]\n",
+      "loss: 0.595583  [  132/  240]\n",
+      "Epoch 854\n",
+      "-------------------------------\n",
+      "loss: 0.572661  [   12/  240]\n",
+      "loss: 0.546563  [  132/  240]\n",
+      "Epoch 855\n",
+      "-------------------------------\n",
+      "loss: 0.551530  [   12/  240]\n",
+      "loss: 0.550175  [  132/  240]\n",
+      "Epoch 856\n",
+      "-------------------------------\n",
+      "loss: 0.554807  [   12/  240]\n",
+      "loss: 0.581791  [  132/  240]\n",
+      "Epoch 857\n",
+      "-------------------------------\n",
+      "loss: 0.575758  [   12/  240]\n",
+      "loss: 24.745825  [  132/  240]\n",
+      "Epoch 858\n",
+      "-------------------------------\n",
+      "loss: 24.727196  [   12/  240]\n",
+      "loss: 0.575433  [  132/  240]\n",
+      "Epoch 859\n",
+      "-------------------------------\n",
+      "loss: 0.568117  [   12/  240]\n",
+      "loss: 18.908209  [  132/  240]\n",
+      "Epoch 860\n",
+      "-------------------------------\n",
+      "loss: 0.567824  [   12/  240]\n",
+      "loss: 24.713242  [  132/  240]\n",
+      "Epoch 861\n",
+      "-------------------------------\n",
+      "loss: 0.557697  [   12/  240]\n",
+      "loss: 1.304673  [  132/  240]\n",
+      "Epoch 862\n",
+      "-------------------------------\n",
+      "loss: 0.547338  [   12/  240]\n",
+      "loss: 0.555230  [  132/  240]\n",
+      "Epoch 863\n",
+      "-------------------------------\n",
+      "loss: 0.553073  [   12/  240]\n",
+      "loss: 1.267208  [  132/  240]\n",
+      "Epoch 864\n",
+      "-------------------------------\n",
+      "loss: 0.572026  [   12/  240]\n",
+      "loss: 0.568082  [  132/  240]\n",
+      "Epoch 865\n",
+      "-------------------------------\n",
+      "loss: 0.556957  [   12/  240]\n",
+      "loss: 24.717546  [  132/  240]\n",
+      "Epoch 866\n",
+      "-------------------------------\n",
+      "loss: 0.563536  [   12/  240]\n",
+      "loss: 0.569209  [  132/  240]\n",
+      "Epoch 867\n",
+      "-------------------------------\n",
+      "loss: 18.927540  [   12/  240]\n",
+      "loss: 0.549061  [  132/  240]\n",
+      "Epoch 868\n",
+      "-------------------------------\n",
+      "loss: 0.559638  [   12/  240]\n",
+      "loss: 0.585070  [  132/  240]\n",
+      "Epoch 869\n",
+      "-------------------------------\n",
+      "loss: 0.557294  [   12/  240]\n",
+      "loss: 0.577373  [  132/  240]\n",
+      "Epoch 870\n",
+      "-------------------------------\n",
+      "loss: 0.562661  [   12/  240]\n",
+      "loss: 0.572184  [  132/  240]\n",
+      "Epoch 871\n",
+      "-------------------------------\n",
+      "loss: 0.551253  [   12/  240]\n",
+      "loss: 24.751759  [  132/  240]\n",
+      "Epoch 872\n",
+      "-------------------------------\n",
+      "loss: 18.914881  [   12/  240]\n",
+      "loss: 24.707706  [  132/  240]\n",
+      "Epoch 873\n",
+      "-------------------------------\n",
+      "loss: 0.571800  [   12/  240]\n",
+      "loss: 0.560709  [  132/  240]\n",
+      "Epoch 874\n",
+      "-------------------------------\n",
+      "loss: 0.574329  [   12/  240]\n",
+      "loss: 0.549192  [  132/  240]\n",
+      "Epoch 875\n",
+      "-------------------------------\n",
+      "loss: 0.541225  [   12/  240]\n",
+      "loss: 0.539478  [  132/  240]\n",
+      "Epoch 876\n",
+      "-------------------------------\n",
+      "loss: 0.559529  [   12/  240]\n",
+      "loss: 18.920176  [  132/  240]\n",
+      "Epoch 877\n",
+      "-------------------------------\n",
+      "loss: 0.563715  [   12/  240]\n",
+      "loss: 24.747883  [  132/  240]\n",
+      "Epoch 878\n",
+      "-------------------------------\n",
+      "loss: 0.574023  [   12/  240]\n",
+      "loss: 0.601463  [  132/  240]\n",
+      "Epoch 879\n",
+      "-------------------------------\n",
+      "loss: 0.560112  [   12/  240]\n",
+      "loss: 0.547417  [  132/  240]\n",
+      "Epoch 880\n",
+      "-------------------------------\n",
+      "loss: 0.552557  [   12/  240]\n",
+      "loss: 0.562537  [  132/  240]\n",
+      "Epoch 881\n",
+      "-------------------------------\n",
+      "loss: 18.935055  [   12/  240]\n",
+      "loss: 0.559289  [  132/  240]\n",
+      "Epoch 882\n",
+      "-------------------------------\n",
+      "loss: 0.584476  [   12/  240]\n",
+      "loss: 18.885115  [  132/  240]\n",
+      "Epoch 883\n",
+      "-------------------------------\n",
+      "loss: 0.536230  [   12/  240]\n",
+      "loss: 0.571675  [  132/  240]\n",
+      "Epoch 884\n",
+      "-------------------------------\n",
+      "loss: 1.010345  [   12/  240]\n",
+      "loss: 0.582327  [  132/  240]\n",
+      "Epoch 885\n",
+      "-------------------------------\n",
+      "loss: 0.570520  [   12/  240]\n",
+      "loss: 0.564556  [  132/  240]\n",
+      "Epoch 886\n",
+      "-------------------------------\n",
+      "loss: 0.584466  [   12/  240]\n",
+      "loss: 0.581567  [  132/  240]\n",
+      "Epoch 887\n",
+      "-------------------------------\n",
+      "loss: 0.575978  [   12/  240]\n",
+      "loss: 0.567958  [  132/  240]\n",
+      "Epoch 888\n",
+      "-------------------------------\n",
+      "loss: 0.553877  [   12/  240]\n",
+      "loss: 0.570101  [  132/  240]\n",
+      "Epoch 889\n",
+      "-------------------------------\n",
+      "loss: 1.071415  [   12/  240]\n",
+      "loss: 0.596139  [  132/  240]\n",
+      "Epoch 890\n",
+      "-------------------------------\n",
+      "loss: 0.587762  [   12/  240]\n",
+      "loss: 0.556013  [  132/  240]\n",
+      "Epoch 891\n",
+      "-------------------------------\n",
+      "loss: 0.569597  [   12/  240]\n",
+      "loss: 0.559838  [  132/  240]\n",
+      "Epoch 892\n",
+      "-------------------------------\n",
+      "loss: 24.728004  [   12/  240]\n",
+      "loss: 0.533917  [  132/  240]\n",
+      "Epoch 893\n",
+      "-------------------------------\n",
+      "loss: 0.557951  [   12/  240]\n",
+      "loss: 0.533760  [  132/  240]\n",
+      "Epoch 894\n",
+      "-------------------------------\n",
+      "loss: 0.571287  [   12/  240]\n",
+      "loss: 1.276787  [  132/  240]\n",
+      "Epoch 895\n",
+      "-------------------------------\n",
+      "loss: 0.573536  [   12/  240]\n",
+      "loss: 0.567644  [  132/  240]\n",
+      "Epoch 896\n",
+      "-------------------------------\n",
+      "loss: 0.585014  [   12/  240]\n",
+      "loss: 0.557379  [  132/  240]\n",
+      "Epoch 897\n",
+      "-------------------------------\n",
+      "loss: 18.908770  [   12/  240]\n",
+      "loss: 0.574391  [  132/  240]\n",
+      "Epoch 898\n",
+      "-------------------------------\n",
+      "loss: 0.553482  [   12/  240]\n",
+      "loss: 24.740828  [  132/  240]\n",
+      "Epoch 899\n",
+      "-------------------------------\n",
+      "loss: 18.909206  [   12/  240]\n",
+      "loss: 0.568669  [  132/  240]\n",
+      "Epoch 900\n",
+      "-------------------------------\n",
+      "loss: 0.544842  [   12/  240]\n",
+      "loss: 1.286197  [  132/  240]\n",
+      "Epoch 901\n",
+      "-------------------------------\n",
+      "loss: 0.557597  [   12/  240]\n",
+      "loss: 0.538427  [  132/  240]\n",
+      "Epoch 902\n",
+      "-------------------------------\n",
+      "loss: 24.738150  [   12/  240]\n",
+      "loss: 1.156373  [  132/  240]\n",
+      "Epoch 903\n",
+      "-------------------------------\n",
+      "loss: 0.551991  [   12/  240]\n",
+      "loss: 18.930983  [  132/  240]\n",
+      "Epoch 904\n",
+      "-------------------------------\n",
+      "loss: 0.551765  [   12/  240]\n",
+      "loss: 0.576845  [  132/  240]\n",
+      "Epoch 905\n",
+      "-------------------------------\n",
+      "loss: 0.578323  [   12/  240]\n",
+      "loss: 0.577951  [  132/  240]\n",
+      "Epoch 906\n",
+      "-------------------------------\n",
+      "loss: 0.568033  [   12/  240]\n",
+      "loss: 0.550570  [  132/  240]\n",
+      "Epoch 907\n",
+      "-------------------------------\n",
+      "loss: 0.576590  [   12/  240]\n",
+      "loss: 0.565554  [  132/  240]\n",
+      "Epoch 908\n",
+      "-------------------------------\n",
+      "loss: 0.557564  [   12/  240]\n",
+      "loss: 0.559268  [  132/  240]\n",
+      "Epoch 909\n",
+      "-------------------------------\n",
+      "loss: 1.121924  [   12/  240]\n",
+      "loss: 18.971920  [  132/  240]\n",
+      "Epoch 910\n",
+      "-------------------------------\n",
+      "loss: 24.715303  [   12/  240]\n",
+      "loss: 0.557605  [  132/  240]\n",
+      "Epoch 911\n",
+      "-------------------------------\n",
+      "loss: 0.562292  [   12/  240]\n",
+      "loss: 0.538912  [  132/  240]\n",
+      "Epoch 912\n",
+      "-------------------------------\n",
+      "loss: 0.564494  [   12/  240]\n",
+      "loss: 0.542657  [  132/  240]\n",
+      "Epoch 913\n",
+      "-------------------------------\n",
+      "loss: 0.549499  [   12/  240]\n",
+      "loss: 0.558729  [  132/  240]\n",
+      "Epoch 914\n",
+      "-------------------------------\n",
+      "loss: 0.576176  [   12/  240]\n",
+      "loss: 0.549210  [  132/  240]\n",
+      "Epoch 915\n",
+      "-------------------------------\n",
+      "loss: 0.568132  [   12/  240]\n",
+      "loss: 0.536147  [  132/  240]\n",
+      "Epoch 916\n",
+      "-------------------------------\n",
+      "loss: 0.562466  [   12/  240]\n",
+      "loss: 0.567821  [  132/  240]\n",
+      "Epoch 917\n",
+      "-------------------------------\n",
+      "loss: 1.137234  [   12/  240]\n",
+      "loss: 0.561681  [  132/  240]\n",
+      "Epoch 918\n",
+      "-------------------------------\n",
+      "loss: 0.570843  [   12/  240]\n",
+      "loss: 0.555417  [  132/  240]\n",
+      "Epoch 919\n",
+      "-------------------------------\n",
+      "loss: 18.930506  [   12/  240]\n",
+      "loss: 0.564843  [  132/  240]\n",
+      "Epoch 920\n",
+      "-------------------------------\n",
+      "loss: 0.554281  [   12/  240]\n",
+      "loss: 0.579297  [  132/  240]\n",
+      "Epoch 921\n",
+      "-------------------------------\n",
+      "loss: 0.570739  [   12/  240]\n",
+      "loss: 0.579613  [  132/  240]\n",
+      "Epoch 922\n",
+      "-------------------------------\n",
+      "loss: 0.564136  [   12/  240]\n",
+      "loss: 18.909384  [  132/  240]\n",
+      "Epoch 923\n",
+      "-------------------------------\n",
+      "loss: 0.578886  [   12/  240]\n",
+      "loss: 0.562575  [  132/  240]\n",
+      "Epoch 924\n",
+      "-------------------------------\n",
+      "loss: 0.559168  [   12/  240]\n",
+      "loss: 0.569506  [  132/  240]\n",
+      "Epoch 925\n",
+      "-------------------------------\n",
+      "loss: 0.571277  [   12/  240]\n",
+      "loss: 0.545417  [  132/  240]\n",
+      "Epoch 926\n",
+      "-------------------------------\n",
+      "loss: 0.559596  [   12/  240]\n",
+      "loss: 0.568056  [  132/  240]\n",
+      "Epoch 927\n",
+      "-------------------------------\n",
+      "loss: 0.546493  [   12/  240]\n",
+      "loss: 0.551044  [  132/  240]\n",
+      "Epoch 928\n",
+      "-------------------------------\n",
+      "loss: 24.681021  [   12/  240]\n",
+      "loss: 0.546538  [  132/  240]\n",
+      "Epoch 929\n",
+      "-------------------------------\n",
+      "loss: 0.558309  [   12/  240]\n",
+      "loss: 24.732309  [  132/  240]\n",
+      "Epoch 930\n",
+      "-------------------------------\n",
+      "loss: 0.557287  [   12/  240]\n",
+      "loss: 18.928307  [  132/  240]\n",
+      "Epoch 931\n",
+      "-------------------------------\n",
+      "loss: 0.567440  [   12/  240]\n",
+      "loss: 0.574792  [  132/  240]\n",
+      "Epoch 932\n",
+      "-------------------------------\n",
+      "loss: 18.923805  [   12/  240]\n",
+      "loss: 0.550483  [  132/  240]\n",
+      "Epoch 933\n",
+      "-------------------------------\n",
+      "loss: 0.529904  [   12/  240]\n",
+      "loss: 0.537583  [  132/  240]\n",
+      "Epoch 934\n",
+      "-------------------------------\n",
+      "loss: 0.552212  [   12/  240]\n",
+      "loss: 0.542707  [  132/  240]\n",
+      "Epoch 935\n",
+      "-------------------------------\n",
+      "loss: 0.564671  [   12/  240]\n",
+      "loss: 0.568587  [  132/  240]\n",
+      "Epoch 936\n",
+      "-------------------------------\n",
+      "loss: 0.569847  [   12/  240]\n",
+      "loss: 0.560557  [  132/  240]\n",
+      "Epoch 937\n",
+      "-------------------------------\n",
+      "loss: 18.935137  [   12/  240]\n",
+      "loss: 0.569192  [  132/  240]\n",
+      "Epoch 938\n",
+      "-------------------------------\n",
+      "loss: 0.592737  [   12/  240]\n",
+      "loss: 0.550057  [  132/  240]\n",
+      "Epoch 939\n",
+      "-------------------------------\n",
+      "loss: 0.552824  [   12/  240]\n",
+      "loss: 0.565369  [  132/  240]\n",
+      "Epoch 940\n",
+      "-------------------------------\n",
+      "loss: 1.080996  [   12/  240]\n",
+      "loss: 0.580145  [  132/  240]\n",
+      "Epoch 941\n",
+      "-------------------------------\n",
+      "loss: 0.571089  [   12/  240]\n",
+      "loss: 0.542820  [  132/  240]\n",
+      "Epoch 942\n",
+      "-------------------------------\n",
+      "loss: 0.553546  [   12/  240]\n",
+      "loss: 24.753477  [  132/  240]\n",
+      "Epoch 943\n",
+      "-------------------------------\n",
+      "loss: 0.583423  [   12/  240]\n",
+      "loss: 0.539761  [  132/  240]\n",
+      "Epoch 944\n",
+      "-------------------------------\n",
+      "loss: 0.548080  [   12/  240]\n",
+      "loss: 0.542365  [  132/  240]\n",
+      "Epoch 945\n",
+      "-------------------------------\n",
+      "loss: 0.534543  [   12/  240]\n",
+      "loss: 0.556860  [  132/  240]\n",
+      "Epoch 946\n",
+      "-------------------------------\n",
+      "loss: 0.557295  [   12/  240]\n",
+      "loss: 0.580892  [  132/  240]\n",
+      "Epoch 947\n",
+      "-------------------------------\n",
+      "loss: 0.584449  [   12/  240]\n",
+      "loss: 0.565156  [  132/  240]\n",
+      "Epoch 948\n",
+      "-------------------------------\n",
+      "loss: 0.568442  [   12/  240]\n",
+      "loss: 0.545456  [  132/  240]\n",
+      "Epoch 949\n",
+      "-------------------------------\n",
+      "loss: 0.564755  [   12/  240]\n",
+      "loss: 0.547114  [  132/  240]\n",
+      "Epoch 950\n",
+      "-------------------------------\n",
+      "loss: 0.555418  [   12/  240]\n",
+      "loss: 0.575458  [  132/  240]\n",
+      "Epoch 951\n",
+      "-------------------------------\n",
+      "loss: 0.560388  [   12/  240]\n",
+      "loss: 0.562063  [  132/  240]\n",
+      "Epoch 952\n",
+      "-------------------------------\n",
+      "loss: 0.570293  [   12/  240]\n",
+      "loss: 0.570476  [  132/  240]\n",
+      "Epoch 953\n",
+      "-------------------------------\n",
+      "loss: 0.560181  [   12/  240]\n",
+      "loss: 1.218647  [  132/  240]\n",
+      "Epoch 954\n",
+      "-------------------------------\n",
+      "loss: 0.571142  [   12/  240]\n",
+      "loss: 0.557877  [  132/  240]\n",
+      "Epoch 955\n",
+      "-------------------------------\n",
+      "loss: 24.691566  [   12/  240]\n",
+      "loss: 0.562933  [  132/  240]\n",
+      "Epoch 956\n",
+      "-------------------------------\n",
+      "loss: 18.931520  [   12/  240]\n",
+      "loss: 0.588673  [  132/  240]\n",
+      "Epoch 957\n",
+      "-------------------------------\n",
+      "loss: 24.736883  [   12/  240]\n",
+      "loss: 18.895466  [  132/  240]\n",
+      "Epoch 958\n",
+      "-------------------------------\n",
+      "loss: 0.536820  [   12/  240]\n",
+      "loss: 0.553096  [  132/  240]\n",
+      "Epoch 959\n",
+      "-------------------------------\n",
+      "loss: 18.915905  [   12/  240]\n",
+      "loss: 0.551370  [  132/  240]\n",
+      "Epoch 960\n",
+      "-------------------------------\n",
+      "loss: 0.528893  [   12/  240]\n",
+      "loss: 0.557643  [  132/  240]\n",
+      "Epoch 961\n",
+      "-------------------------------\n",
+      "loss: 0.566405  [   12/  240]\n",
+      "loss: 0.569439  [  132/  240]\n",
+      "Epoch 962\n",
+      "-------------------------------\n",
+      "loss: 0.561282  [   12/  240]\n",
+      "loss: 18.935314  [  132/  240]\n",
+      "Epoch 963\n",
+      "-------------------------------\n",
+      "loss: 0.559000  [   12/  240]\n",
+      "loss: 0.557844  [  132/  240]\n",
+      "Epoch 964\n",
+      "-------------------------------\n",
+      "loss: 0.549401  [   12/  240]\n",
+      "loss: 0.564700  [  132/  240]\n",
+      "Epoch 965\n",
+      "-------------------------------\n",
+      "loss: 0.558038  [   12/  240]\n",
+      "loss: 0.558092  [  132/  240]\n",
+      "Epoch 966\n",
+      "-------------------------------\n",
+      "loss: 1.224825  [   12/  240]\n",
+      "loss: 0.580149  [  132/  240]\n",
+      "Epoch 967\n",
+      "-------------------------------\n",
+      "loss: 0.576548  [   12/  240]\n",
+      "loss: 0.535631  [  132/  240]\n",
+      "Epoch 968\n",
+      "-------------------------------\n",
+      "loss: 0.577351  [   12/  240]\n",
+      "loss: 0.568448  [  132/  240]\n",
+      "Epoch 969\n",
+      "-------------------------------\n",
+      "loss: 0.566807  [   12/  240]\n",
+      "loss: 0.565736  [  132/  240]\n",
+      "Epoch 970\n",
+      "-------------------------------\n",
+      "loss: 0.551953  [   12/  240]\n",
+      "loss: 0.563645  [  132/  240]\n",
+      "Epoch 971\n",
+      "-------------------------------\n",
+      "loss: 0.569364  [   12/  240]\n",
+      "loss: 0.589471  [  132/  240]\n",
+      "Epoch 972\n",
+      "-------------------------------\n",
+      "loss: 1.096179  [   12/  240]\n",
+      "loss: 0.557045  [  132/  240]\n",
+      "Epoch 973\n",
+      "-------------------------------\n",
+      "loss: 0.563234  [   12/  240]\n",
+      "loss: 0.539231  [  132/  240]\n",
+      "Epoch 974\n",
+      "-------------------------------\n",
+      "loss: 0.525148  [   12/  240]\n",
+      "loss: 0.552967  [  132/  240]\n",
+      "Epoch 975\n",
+      "-------------------------------\n",
+      "loss: 0.563263  [   12/  240]\n",
+      "loss: 0.568076  [  132/  240]\n",
+      "Epoch 976\n",
+      "-------------------------------\n",
+      "loss: 0.553058  [   12/  240]\n",
+      "loss: 1.193116  [  132/  240]\n",
+      "Epoch 977\n",
+      "-------------------------------\n",
+      "loss: 0.575798  [   12/  240]\n",
+      "loss: 0.593945  [  132/  240]\n",
+      "Epoch 978\n",
+      "-------------------------------\n",
+      "loss: 0.574578  [   12/  240]\n",
+      "loss: 0.554529  [  132/  240]\n",
+      "Epoch 979\n",
+      "-------------------------------\n",
+      "loss: 0.546704  [   12/  240]\n",
+      "loss: 0.562495  [  132/  240]\n",
+      "Epoch 980\n",
+      "-------------------------------\n",
+      "loss: 0.557020  [   12/  240]\n",
+      "loss: 0.549480  [  132/  240]\n",
+      "Epoch 981\n",
+      "-------------------------------\n",
+      "loss: 0.535374  [   12/  240]\n",
+      "loss: 0.571735  [  132/  240]\n",
+      "Epoch 982\n",
+      "-------------------------------\n",
+      "loss: 0.556190  [   12/  240]\n",
+      "loss: 0.546381  [  132/  240]\n",
+      "Epoch 983\n",
+      "-------------------------------\n",
+      "loss: 0.547579  [   12/  240]\n",
+      "loss: 0.559418  [  132/  240]\n",
+      "Epoch 984\n",
+      "-------------------------------\n",
+      "loss: 0.573269  [   12/  240]\n",
+      "loss: 0.563796  [  132/  240]\n",
+      "Epoch 985\n",
+      "-------------------------------\n",
+      "loss: 0.550604  [   12/  240]\n",
+      "loss: 0.544625  [  132/  240]\n",
+      "Epoch 986\n",
+      "-------------------------------\n",
+      "loss: 1.133788  [   12/  240]\n",
+      "loss: 0.564795  [  132/  240]\n",
+      "Epoch 987\n",
+      "-------------------------------\n",
+      "loss: 25.220053  [   12/  240]\n",
+      "loss: 0.560604  [  132/  240]\n",
+      "Epoch 988\n",
+      "-------------------------------\n",
+      "loss: 0.566148  [   12/  240]\n",
+      "loss: 24.716599  [  132/  240]\n",
+      "Epoch 989\n",
+      "-------------------------------\n",
+      "loss: 24.711941  [   12/  240]\n",
+      "loss: 0.551060  [  132/  240]\n",
+      "Epoch 990\n",
+      "-------------------------------\n",
+      "loss: 0.556031  [   12/  240]\n",
+      "loss: 25.444288  [  132/  240]\n",
+      "Epoch 991\n",
+      "-------------------------------\n",
+      "loss: 0.557110  [   12/  240]\n",
+      "loss: 0.545487  [  132/  240]\n",
+      "Epoch 992\n",
+      "-------------------------------\n",
+      "loss: 0.577074  [   12/  240]\n",
+      "loss: 0.561198  [  132/  240]\n",
+      "Epoch 993\n",
+      "-------------------------------\n",
+      "loss: 0.576803  [   12/  240]\n",
+      "loss: 0.544005  [  132/  240]\n",
+      "Epoch 994\n",
+      "-------------------------------\n",
+      "loss: 0.550534  [   12/  240]\n",
+      "loss: 0.544543  [  132/  240]\n",
+      "Epoch 995\n",
+      "-------------------------------\n",
+      "loss: 0.565406  [   12/  240]\n",
+      "loss: 0.544614  [  132/  240]\n",
+      "Epoch 996\n",
+      "-------------------------------\n",
+      "loss: 0.581785  [   12/  240]\n",
+      "loss: 0.565919  [  132/  240]\n",
+      "Epoch 997\n",
+      "-------------------------------\n",
+      "loss: 24.720743  [   12/  240]\n",
+      "loss: 0.550393  [  132/  240]\n",
+      "Epoch 998\n",
+      "-------------------------------\n",
+      "loss: 0.529685  [   12/  240]\n",
+      "loss: 0.569524  [  132/  240]\n",
+      "Epoch 999\n",
+      "-------------------------------\n",
+      "loss: 0.540554  [   12/  240]\n",
+      "loss: 0.558723  [  132/  240]\n",
+      "Epoch 1000\n",
+      "-------------------------------\n",
+      "loss: 0.574826  [   12/  240]\n",
+      "loss: 0.581278  [  132/  240]\n",
+      "Done!\n"
+     ]
+    }
+   ],
+   "source": [
+    "def train_loop(dataloader, model, loss_func, optimizer):\n",
+    "    model.train()\n",
+    "    for batch, X in enumerate(dataloader):\n",
+    "        X = X\n",
+    "        size = len(dataloader.dataset)\n",
+    "\n",
+    "        input_pos = X.pos.contiguous()\n",
+    "        input_velo = X.velo.contiguous()\n",
+    "        input_edge_index = X.edge_index.contiguous()\n",
+    "        output_pos = X.output_pos.contiguous() # [timesteps, num_nodes, 3]\n",
+    "\n",
+    "        pred = model(None, input_pos, input_edge_index, torch.zeros(input_edge_index.shape[1], 1), input_velo)\n",
+    "        \n",
+    "        output_pos = output_pos.transpose(0,1)\n",
+    "        loss = loss_func(pred[1], output_pos)\n",
+    "        \n",
+    "        loss.backward()\n",
+    "        optimizer.step()\n",
+    "        optimizer.zero_grad()\n",
+    "\n",
+    "        loss, current = loss.item(), (batch+1) * batch_size\n",
+    "        if batch % 10 == 0:\n",
+    "            print(f\"loss: {loss:>7f}  [{current:>5d}/{size:>5d}]\")\n",
+    "    return loss\n",
+    "\n",
+    "epoch_count, loss = [], []\n",
+    "for t in range(epochs):\n",
+    "    print(f\"Epoch {t+1}\\n-------------------------------\")\n",
+    "    cur_loss = train_loop(dataloader, model, loss_func, optimizer)\n",
+    "    epoch_count.append(t)\n",
+    "    loss.append(cur_loss)\n",
+    "print(\"Done!\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "4cacc1c5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "plt.plot(epoch_count, loss)\n",
+    "plt.xlabel('Epochs')\n",
+    "plt.ylabel('MSE')\n",
+    "# plt.axis((0,1000,0, 20))\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "6d3e3e8d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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Qk5OTXH311QwPD+N2u3G5XJjNZqxWq6IdhmrvYoKF94kavnygvjefeKb8+kXz2M2bN9PS0qKZxyqMJkxpIBKJ0NXVJZmpVldXr2imCumxBxLPVeLMWOeP0tElp8R54XCY/v5+fD4foVBo2Q5CnU5HZWUllZWVBAIB3G43+/fvp7q6GpvNRmlpadLXIp6jBuLPTn5eKn35xDPVjkrC4fCy6Ve5eaz4Hmhvb5dcJYqLi7U0X5xowqQiMzMzeDwevF4vELuZKqTHhQGSi5gS9a9bSxFTMBiku7ubrq4usrOzpQHnWG6cxcXFbN26lZaWFrxeL0ePHqW4uBibzUZVVVXC16bmjXspYRJJhS8fpDeVtxIGg4H6+nrq6uok89h9+/ZRXl6O3W6npqZGS/PFiCZMKUYQBMbHx3E6nQmbqYqkQ5gSiZiSmT9aKxHTzMwMfr+f3t5eioqKaG1tpaKiIiExyc/Pp7W1FYfDgd/v59y5c2RlZWG1WjN+4d1KwiSipC8fZEYqbyWWMo89efIkWVlZmnlsjGjClCIikQi9vb24XK6kzVRFMj1iUsq/LpMjJnlDQ1VVFdu3b6e4uFiRa8nOzsZut2O1Wunt7cXtdtPR0YHFYokrBabmXFEswiRHiTpUOlJ5kUgkITGJNo/1er2aeWwMaMKkMPPz83g8HjweD6FQiPr6etatW6fIvINeryccDitwlbETS8SkpH9dJkZM0Q0NdXV17N69O2UzLPKuzKGhIalRwmQyKd4okSxq+/JBelJ54XA4qWYGuXms2FqvmccujyZMCqCGmSpkVsSUKv+6TIqYIpEIfX19+P1+gsFgSiyRVru2qqoqqqqqGB8fx+Px8NJLL0mNEiUlJUv+O7UjpmTOSqQOlempvJXQ6/VUVFRQXl6umceugPYTSALRTNXtdjM0NJRSM1XIjBpTqv3rMiFikjc0iDfOdG89LSkpYevWrVIDzZEjRxRplEgWtX35xDMzsfkhHlYyjxVdJV7L5rGaMCVAOsxUIb0RkxL1o1hIZ8Q0MzMjvcZkGxpSRX5+PuvXr6epqWlRo4TNZqO+vl76fa2ViGkpVqtDZWpXXqKsZh6bae9BNdCEKUYEQWB6ehqn04nP5yM7O5uGhgbVzFQhPcIUCoWkhWpK7D9ajXRETFNTU3R3dzMwMKB4Q0OqkDdK9PT04PF46OjooLGxkXA4rNqNLJVpteXqUMFgUHJCUYuV5piUQm4eOzU1hc/n49ChQxQUFNDU1PSaMo/VhGkVos1Uy8rKyM7OxuFwUFNTo+q1GAwGVYRJXj8aHx+nsLBQtf1HagmT6LoxPz9PR0eH5NCw1kw59Xo9ZrMZk8kkNUqMjo4yOztLXV1dyn9naqTVoutQJ06c4JVXXsFsNqfcl09EzShNp9NhNBrZsGEDU1NTGAwG2trauHDhAhaLBavVuubep/GiCdMyiGaqHo+HqakpyUy1sLCQV155JS2O1KmOmJaqHxmNRgRBUK0TLNXCJNYFfT4f8/Pz6PV6Wltbqa6uTtmZaiBvlDh69Cjz8/Ps37+fmpqaFRslkkXNtKFYh9LpdGzdupX+/v6U+/KJpCN9CAs/3+rqarZt2yaZx3Z2dlJXV4fD4VDFfT8daMIUxVJmqlu3bl1U3DcYDKq3bUPqhGml+pHL5WJubk7xM5cjVcIUCoXo7u7G7/eTlZVFQ0MDtbW1HD169LLrgsrJyaGiooLa2lq8Xi9HjhyRTEYrKysVvZGpvYsJFkSiqKiImpqaS+pQVqs1JY0q4XA4LcIkphCXMo996aWXLlvz2MvrE5kgcjPV/v5+iouLaW5uXna2QK2UWjRKC1Ms80epXnsRjdLCJE7ei64b69atu+TmfLntYxLFoqCggPXr10uOEmfOnCEnJ0dylFDiRpaO7bXwZ1Pj6DpUZ2cnbW1tivnyiUQikbTUd6LPXc081mq1xmRxlum8poVpKTPVHTt2rDqNnY5BV/HcZIUp3vkjNRcFiucpIRQTExP4fD4GBgaorKyUXDeWOu9yJycnRzIZ7enpucRRIplW/3RsrwUuEdVU+fLJz01nxLQUS5nHdnR0YDKZ1rx57GtSmGZnZ6V0XSQSwWQyxWymCmszlZfo/NFaipjEyNfn8zE+Ph7z0O/lGjFFYzAYaGhowGw2Mzg4iNvtxul0Sqs3Eimop0uYVnLhX2oeqrKyEovFknAdKhOFSeRyNI99zQjTUmaqohVIvCF6OlN58bbJJjt/tBYiJrGhwe/3Mzc3h9lsZuPGjTGlcdbqE2UyyG/eY2NjeDwe9u3bR21tLTabLa5W+XSn8lZiuXmoROpQ6a4xxUK0eaz4mrOzs6UdUWvFPPayFybRTNXtdjMyMqKYmWowGFT4SmM7N9ZITSn/ukyOmEKhED09Pfj9fvR6fcI2UK+ViGkpSktL2bZtG9PT03g8Hl5++WVKS0uxWq0xNUpkSipvJZKtQwmCgCAIaakxJTo/Jb5mh8NxiXmsw+GgsLAwox/KLlthEs1UvV4vwWCQuro6WlpaFOn/NxgMzM7OKnCV8bFaKi8V/nWZGDGJUWB3dzdGo1Ha+puoaawGFBQUsGHDBpqamvD5fFKjhM1mW9H1O9NSeSuRaB0qETFUgkgkkvSc2FLmsf/zP/+T8eaxl5UwiWaqLpeLrq4u8vPzpbZgJZ92Mq0rL5X+dZkUMU1OTkoNDRUVFcs2NMTLazliiiYnJ4empiZsNtsitwWr1YrZbL7kPaW2oaoSA73x1qHSKUyAIuMMS5nHHjt2jLy8POx2Ow0NDRk1NpE5V5IEkUiEgYEBXC6XamaqmdD8oIZ/XbojJkEQpCe9sbEx6urquOqqqxQb+NUipqWRN0oMDAzg8Xjo7OykoaEBi8UiZR7SETEp+f6OpQ4lftbTsWpD6XOXMo91Op1cuHCBxsZG7HZ7yj0/Y2FNC9PExARPPfUUjY2NlJSUqGammu6uPCX3H8VyZjoipnA4zODgID6fj7m5OUwmExs2bFBsLkWOFjEtj06no6amhpqaGsbGxnC73ezfv5/a2lqsVqvqTt+pitBWqkOJaeJ0CFMqz402j/V6vfzhD3/ICPPYNSlMPT09PPDAA/zkJz/BZDLxyCOPcPXVV1/WZqqCIDA1NcXExAQnTpxQbP/RaqgdMYmCf/jwYfR6vWSUm6rCs5ou3GoJYKqimNLSUrZv387U1BQej4fDhw+Tn5+PwWBQLXJKtRAuVYdyOp3AgtmvGr58ImoYx8Kl5rFer5fDhw9jtVrZuHFjWsRpTQmT1+vl05/+NP/2b//G1q1bKSgo4OTJk6p3y6gZMcnrR6FQCIPBwK5du1Rr+1QrYpqbm8Pv99Pd3Q1AU1OT5IuWalLtzdfX14fX6wXgxIkT2Gw2SktLU3ZmqiksLGTjxo00Nzdz5swZhoaGOHjwIDabLeV7q9SaJ5LXoXp6ejhz5owi81DxoJYwiYjmsRs3bqSiooK2tjY2btyo2vlykvoNf+UrX0Gn03HfffdJfzY7O8tdd91FRUUFRqORW2+9lf7+/mSvE1iYdK6srOTcuXN84xvfSNtsgRrND7Ozs3R2dnLgwAF6e3ux2Wxs2LCBrKwsVWcRUh0dTk5Ocv78eQ4dOsT09LT0QVDa0205UnVGKBTC6/Vy8OBB/H4/5eXlwMI+paNHj3LkyBEGBwdTIopqRWY5OTlUVlZSWVlJQ0MDnZ2d7Nu3D7fbnbK1FOnYXpufn09ubi7XXnstRqORkydPcujQIXp6elJuqpyuNReCIJCdnb32UnlHjx7lu9/9Llu2bFn05x/72Mf43e9+xy9+8QtKSkq4++67efvb386BAweSvtja2lqeeOIJYMFsdWZmJi0eVqlsflipfjQ2NpaWDbZK3+iiGxpqa2sl53bxhqZm3UfJs+bm5qRW9sLCQmnZYFdXFwMDA7S2tuJwOKSWbHGlttKRhpopSnGGrKGhgYGBAclRQmyUyMvLU/w8NRGjNLV8+UTSKUzpPBsSFKbJyUn+5m/+hn/5l3/hi1/8ovTn4+PjPPPMMzz33HO84Q1vAOD73/8+69ev5/Dhw+zevVuZq2Yh3x2JRJiamlJ9qZvSqbxY54/StcFWqTPF7km/38/MzAxms/mShgb5+mw1UOoGPj09jc/no6+vj7KyMrZs2bJsuk7c52W1Wunu7qajo4OOjg6pJTvZG0K6NtjKGyVGR0dxu92LHCWKioqSPi8Tttem2pdPJF0ZIViI+NPZPp7QyXfddRdvectbuOmmmxYJ0/HjxwkGg9x0003Sn7W2ttLY2MihQ4cUFyZYiDDUFibxZp3sDSDe+aN0CJMSEVMoFJJeJ0BjY+OyDQ1qC1OyZ01MTOD1ehkaGqK6uporr7wSo9EY0781GAxSV1R/fz8ulwun0ymZq6aiA1FplvsMlJWVUVZWJjVKHDp0iPLycmw2W1L1mXSk8pYTw1T58omkO2JaU8L005/+lFdeeYWjR49e8nd9fX3k5ORc8qRYU1NDX19fwhe5FAUFBej1egKBgKLfNxbEN0uiacRE54/WWsQkT2vl5+fT1NREVVXVih/WtRAxialIr9fL+Pi4tP020ZSVXq+nrq6O2tpahoeHcblcuFwuzGYzNpst7u+brohpKcRGCdFRItm9SelI5cUiEEr68omka9UGrLFUnt/v59577+WFF15QNG+cCAaDgfz8/DUlTMnOHykVqcWDGDHFc+bU1BQ+n4/+/n7Ky8vjep2ZHDEJgiBtEZ2dncVsNrNp0ybFmlF0Op3UTDA+Po7L5WLfvn3U1dVhs9lijsTUJNYIJjc3l+bmZux2O93d3XR2di5ylIj16TwTUnkroWQdKp3isKZSecePH2dgYIArrrhC+rNwOMy+fft48skn+e///m/m5+cZGxtbFDX19/dTW1ur2EWL5OfnMzExofj3XQ3xTRoOh1e9KcnrR4FAIKn5I/FcNYUp1jMFQWBsbAyfz8fo6OiiVfTxkur16tFnrUY4HKavr09ak9LY2Eh9fX1KbxolJSXSzJDb7ebgwYNUVlZis9koKytb8d9mUsQUjZi+bGhooL+/H7fbvchRYrUH3kxK5a2EEnWodKfy0plKjkuYbrzxRs6cObPoz+644w5aW1v51Kc+JS0de/HFF7n11lsBaGtrw+fzsWfPHuWumj9ba6QjYhKnsVdqgJDXj8LhMA0NDUn714kfDjWfGsWbwHJnRiIRyaFhZmYGk8lEa2trUls01RQmWD5iCoVCdHV10dXVRXZ2dkosn1ajsLCQTZs20dTUhNfr5dixYxQXF2O321VrqV+JRFNrOp2O2traRY4SYnRotVqXbZTI9IgpmmTqUOkWpjUTMRUVFbFp06ZFf1ZYWEhFRYX05x/84Ae5//77KS8vp7i4mHvuuYc9e/Yo2vggkp+fz+TkpOLfNxaWm2VKpX+dPFJT0+UCLr15Rzc0iA4NSlxXuiMm+bCv0WiUWr7TKQJ5eXmsW7cOu92+aE263W6/pIaRyRFTNDqdTmqUmJycXLVRIlNrTLEQbx1KS+UpyGOPPYZer+fWW29lbm6Om2++mX/6p39S+hhgQRTTETHBpbNMavjXyaMXtYiu+UQ3NDgcDsWt89MVMU1PT+P1eqXamFLu5UqSnZ2N3W7HYrFINYz29nZsNhsmk0m6magpTEphNBrZtGkTzc3Ni27cNptN2sK6VlJ5KxFrHSqWUkGqEF1m0kXSwvSnP/1p0X/n5eXx1FNP8dRTTyX7rVeloKAgrRFTKBRicHBQkfpRLIgpxHQI0+TkJP39/fT391NWVpZS41i1I6bZ2VnOnDnD8PAw1dXVS9bGurt1OJ16HI4IJlP6TV/l7t9iq3lnZycWi0XV90cqFuiJjRI2m43u7m7a29tpa2vDarWmZbYnVenD1epQ6UynpVMUYY155UWTLmEKh8OEw2EuXrwIoEj9KFbUFCZxHT3AqVOnqK2tjWtOJ1HUECZBEBgZGWFsbIxQKITZbF625fuHP8zmB/ecIVeY5YJuA5/7diG33ab+BuOlkNdqhoeHcbvdTE5O0tXVRVFRkSKLMVcilWnDrKwsaaZLdJSYmJigsLCQ2dlZ1TqDU13XWq4OlZWVRU5OTlrW16+pGlOmYTQamZqaUu08ef0oEolQV1dHc3Ozqk9wagiT2Bbt8/mYnp5Gp9Oxbdu2VbvBlCKVwiQ2a3i9Xubm5sjJyZHmheR0T3TjHHVSONfERz/aws+Fr3Arv+Ie4Vvce+/d3HhjKCMiJxF5q/nevXsJBoPSegqlXBeWQo2bpl6vl8T31KlTjI2NqdpGHw6Hk2rmiQd5Herll1+mu7ubsbGxpOahEmHNp/LSSWFhoSoR01L1I4/Hg9FoVD2tkEphEjsJfT4fgiBIDg0HDx5U9ekpFcIkf23wZ/eJCxcuYDAYFqXqXhz5Ad85cg8jcwL9szoiW/8F+wkXAC7shMM6XC49JpP6O7liQa/XY7fbKSgokJoJKioqsNvtij9cqD1Tl5ubS21tLWazGY/Hw8GDB6moqJDa6FNxLenoBMzLyyM3NxeHw0EkEkm5L180WsSUBEVFRYyMjKTke682f9TV1ZWW9eqpcDafn5+X2qLFVcvyTsJMWq8eL8FgkO7ubvx+P7m5uZe8Np1Ox3/8JAceeYQSxrml5EH42D38xzUCxix4/1EB7y0fIteZD4EFYdLrBex29X/38aDT6Ra5Lng8Ho4fP47RaMRut6/qwBEr6dhgm52dLTVKiI4Sr7zyCoWFhVitVqlRQskz0+FZJ84SVVdXp9yXT46WyksSo9HI9PS0ot8z1vmjTFmvngxTU1P4/X7JeHTTpk1LPnWme716IszNzeHz+ejp6aGoqIgNGzZc0nrcPdHNH5ynOPRtgd/xFQTgB2UNXMxaECWA3llAH2agfJINAfDSSKYvvI0Wi9zcXNatW4fD4cDv93Pu3DlpLquuri6pm67aXXLR58k73Lq6umhvb5ccJeRdismemS5hEtNpqfbliz4X0IQpUYqKihSrMcU7f6TGTqalSFaYxIYGn8/HyMhITMajayliktshVVRULNvy/cMzP+Sjf/goESEC9+n57m8MfOhEmB+N/CN/lasDBAbnYD4CuoieppEIYfTMUAgCGZ3KW46srCxsNhsWi4Xu7m6cTuciV/NEbkTpKMwv9ZnMysrCarXS2Ni4pKNEMjWidM0TLXduKnz5os8FTZgSxmg0MjMzk9T3SHT+aK1FTPKGhqmpKUwmE+vWrYvpA5uOFvV4hWl8fByv18vIyMiqdkjdE9189aW7+bfd8K9e+HVPhI/cAm9xQnNgnnudWXBliJ4Z0OsMbP3N32EOPM1FmgAwGDI7lbeaWIgr68VWc3F/UmNjIxaLJe76hdoR00o3Xrkh7sjICB6Ph71791JfX4/Vak2oUSITIqalSNV+qFAohE6n05ofEiXRiEkJ/zqDwUAwqH7LcLwiEb1ao6Ghgfr6+riehtQeeI31PLHl2+v1MjExEbPYOkedfGY9lOXA3U1wLgDtk9BZDj9xlDFy7SitwHQI/ir/C1SfWNg+20ELer3AE0/MZVRHXqLIW81HRkZwu93s3bsXk8mE1WqN6fOg9k071tShTqejoqKCiooKJiYmkmqUSKcwxXKu0vuh0u36AGtcmEpLS5mZmYk5naCkf53BYGBubi6Ry06KWIVJbGjo7u6WtqQmao2UaRGTuHDQ5/MxNzcX9+/RUebgvWfgp7sg1wCPbYV3H4bCeXjgr0Z59tXMX7Yenp98iO8XXwcByGUWgBtvTM3acKWIN70mv4kHAgFcLhcvvfQSNTU12O32FVvN1XxgEc+L9z1cVFTE5s2baW5uxuv1So0SoqPEaj+rdA31xju8rFQdKt0rL2CNC1NRUVFMqbxU+NelM5W30rnT09P4/X56e3spLS1l48aNSbfRpqP5Yanzlmv5jvdDZCoy8cXrn+Szx+7hq5sFCrLg57vLmfz+CBEdzL364/VMA/oI+vIOCEARk0Qimd0qnizFxcVs27aN6elp3G635FsntppHv4/U9q5LJnoR/QYdDgddXV1cvHhRcpRYaXNwOvYiie//RM9Npg4lRkzp9IZc08IkRkzLkUr/ukxrfhAbGkRbHSUdGtKdygsGg1I7uzjbkaw/322bb+NG642cPXcvldn/j9ysEQrfD/oQzLz64z0zDoYIbBtZmJXLZXZNtIor0ZBQUFAgtZrLowyx3V7uoZjOrrxEkDdK9PX1SY0SjY2NNDY2XpIKTkcqT3z4TFYQE6lDaRFTkhQXFzM3N0coFJLSOEruP1qJTGh+EF+rz+djcnISk8mU1CbVWM5UA/HGMzs7i9/vp6enh+Li4iVbvpPBVGRiovSLzM72EA6fZer18OV9UP7qZ3U8CO+8WMCWwBgALbSji4R58cWsjLEkWg6lfka5ubnSjc3v93P+/HnJNLa+vj4twqSUSOj1eurr66mrq1uyxibWZtIlTKI3phLEU4dK9wwTrHFhEqfYA4EApaWliu8/WgmDwZDWVF53d7e0uK6hoYGtW7em7M2kdrt4JBKhu7ubtrY2Kioq2L59O8XFxSk5a2EQ9Ummpv6KcDjAla8H8TarB366YZb7i6vYERjEyDT/l/fzwj1/wV+WVFGx3YxQVwey99hSZq/BYJCRkREEQWBgYECx4dblSMXvSt5q3tPTg9vtpqOjQ/U0bypSh9GNEm63W6rN2Gy2tNSYUhW1xFKH0pofkkScT/nEJz6B0Wjkfe97n2rL3NKRypufnycQCDAxMSHli5Wecl8KtVJ5Ysv3+Pg4JSUlXHXVVSlzahfR6XTodMU4HD+jre3N6HVIA7RNRjgxHuF/lX8VV+AD6IDb+DG3CT+G9y18jaDXI9TWIpjN+KYrGTg7wQzVbNT/gm9+c4rrruukq6tLSpmcPXtWakZJlfdZKqMYvV6P2WzGZDIxMDDA6dOnuXDhAtPT00nPC8VCqgd6i4qKpNqM1+vl+PHjhMNhRkdHMRqNqkWHaqTTlqtD5efnp6ULUU56T0+CV155hQ9+8IMAuFwubrnlFq688krVjA7VTOVNT0/T1tbGwYMHmZubk27ayU7tx0oqU3liOvL48eOcPHmSgoICysvLqampSbkoyQlg5RcLOw8R7z1bX+3O89QPcpFmANxY+CM3MN9oR8jJodsYYW9OD70Xj2A7+59cy37exvNEIjruv78Al2uerVu3Yjab0el0XHfddTQ0NNDR0cFLL70ktfGvNXQ6nfQ7stlsBAIB9u7dy/nz5xV3Y5GjVrOF2Chx3XXXAeB0Otm/fz8+n0+Vz72adR6xDnXddddhNpsZHR3VakzxEolEuPnmmzl48CB33HEHRqORz3/+81x77bWqXocaqTyxoWFoaIjq6mp27NjB6OgogUBA1bx+KtI1kUiE/v5+fD4fwWAQs9nMli1byM7O5syZM6ruYxIEAedIJ6ZXdVAQFsTp6kr43HoYdDzAz1r+mv/T08HoMJx4z9fZdkcFz537dz764seIEEEv6Pir//j/+PWJ3xF59XkvEtGTl7eJ0tIwExMTwML7prGxEbPZTF9fn7RHyWazJey+EI3aG2yLi4tpamoiEAjgdrt56aWXqK6uxm63K56CVbveI561Z88eqQ7V0dGR8DByrKQjfSjWoSYmJtK6iwkSFKann36ap59+Go/HA8DGjRt5+OGHefOb3wwsFK3//u//np/+9KeLttjW1NQkfcF6vZ6PfvSj/OxnP6OsrIxf/vKX0odeTVKVyotuaKivr2fPnj1SQ8P4+LjqT9hK1pjC4TA9PT34fD70ej2NjY3U1tYuekJTuwsQoHDyBNdUQjACX74An92wIE7XVgMIcPuvWNi+5eUKdnDqFLRE4Pevh5F5eNfLAs/f8nu6nGAOBMlnmnlD/rIdfPLC+8DAAE6nE6fTKXWLpfvGEA+iCBYXF7N161amp6fxeDwcPnx42RXpiaK2N5/cnkf8fYl7r9xu9yWNEkqRjhZ1kVAolPI9XquRkDCZzWa+8pWv0NzcjCAI/OAHP+Ctb30rJ06cYOPGjXzsYx/jd7/7Hb/4xS8oKSnh7rvv5u1vfzsHDhxQ5KJvueUW6f8XFBSkRZjEVJ5ST6fhcJi+vj78fj+hUIiGhgYpgog+V21hUiJikg/85uXl0dzcvGwTgJoFdZ1ORyjkQZj5CjodPOOCvUPw/mloLFxoGT87DpWdf807in/NfAWMl+eTXTJD9qsPtGXiQ7M+zPnyLMyBEDX6QT7+RM2qDhFiSqy6uprh4WFcLhcul4vGxkasVmtCNRs1I6alhKKgoIANGzZIreZiijbWgdaVSMfcFPw5cpLvvZI3D1RVVWG1WhVbK5LOlu0125UnFwaARx55hKeffprDhw9jNpt55plneO6553jDG94AwPe//33Wr1/P4cOH2b17d/JXLSM/Pz9tERMk/2Qjn9HJycnBYrGs2NCQDmFKJmKamZmRWr5LSkpiGvhVM2IShCAzM59Fp5um9BX42ufh8M5bebL0lzy6Fcz5cN9JiPz2Lfx/J/q5hoMcZBvXV/wQ0yebeWYn5OihIgfGggaadeXAAP/zcx+VbyqP+TrkN7zR0VFcLhd79+6Vlhim+wl2OVYSwZycHGlFujjQKraam0ymhAQmXRZIS50pNg+IjhLHjh2jqKhIasBKRoDTKUzpXhIICjQ/hMNhfvrTnzI1NcWePXs4fvw4wWCQm266Sfqa1tZWGhsbOXToULLHXUJhYWHahSkRZmZmaG9v58CBA4yNjbFhw4aYGhrWSsQ0OTnJ+fPnefnll5mfn+eKK65g+/btMaV01BSmubnvEYmcI2tKT+tX4PeBv8V75HFeGYWZ8EI09Jd1wC0f4t3FjwKwm8PYh4N4fvYvuF+1atxUrOeJm56g0biQrq7RDSZ8TWVlZezYsYPdu3czPz/P/v37OX36dMxLMdWuMa0mFOJA67XXXovD4cDr9bJ3717cbjehUHz2TulYs7Ha68vPz6e1tZXrr7+empoazp8/n3SjhBYxJciZM2fYs2cPs7OzGI1Gfv3rX7NhwwZOnjxJTk4OpaWli76+pqaGvr6+ZK/3EgoKClRdry4ivlnD4XBc9YBAIIDP52NwcJDq6mquuOKKuArE6YqYYr2BjI2N4fV6GR0dpba2NqGWb7WEaXLyZebmngGg5RsRcvIauO7I5/hS5yk+3QbDc2AugP/dAP/RG6arYp5zgQ1s5Dz/yvu46pVjnD/wJxw3/5ivve79tNpvQ6j85cJrGB5O+vpEe6CpqSncbjcHDx6kqqoKu92+5CqPdBCPCOr1ekwmE/X19QwODuJyuXA6nTQ0NMSctlQ7lRdPE4K448pisdDb23uJo0Q8jRKaMCXIunXrOHnyJOPj4/zbv/0bt99+O3v37lXy2mIiXRGTGN7H8kQkCALDw8P4fD4mJiaor69n9+7dCaVnMjFiim7YMJvNtLa2JjzTosYTcTg8gdt9JxCh+gWo/iNM//uT1K0r4q/rbTzUruf/eiI8vAFq86AuV8cHP2ng9x++mY2cZwPnAYFzJ27ilpt/jC640BohlC+k73RDQ4pda2FhobSt1e12c+TIEUpLS3E4HMv616kZMcV7lnzIU562XK2RQBCEtNSY4hUIuQAPDQ3h8XgWNUrE8qCWLmESBGFtD9jm5OTQ1LSwn2bHjh0cPXqUJ554gne+853Mz88zNja2KGrq7++ntrY26QuOxmg0piVigtU78yKRCH19ffh8PkKhEGazOWk3ikyqMUW3fCvlQJHqiKl7ohu/76Nkz3vIGTTQ/ESY+Q98gPANNxAKhQgOB7nbcjdPep7k6EiEneXwIYfA58/fQPn2R/j7E5DHLEYmaWvbBcDU1AkEIYhQWbnwGhSImKLJy8tj/fr1UjrslVdewWg04nA4qKysTIvpZrIiKKYt5Y4L1dXV2Gy2S6LC6EYENUimpqXT6aiqqqKqqkpqpd+/f7/0+qKzSnLS0S4uP3vNClM0kUiEubk5duzYQXZ2Ni+++CK33norAG1tbfh8Pvbs2aPUcRKFhYWMjo4q/n1jYbmIKRgM0t3dTVdXlzQboNTgbzo8+qLFMBQK0dPTg9/vl+ZylBxsTqUw/fDMDznRcTfvtUBEAPfPw9RPWfjPli9ytdNJV1cXRqORj133Md625218/oU3sLMcrq2E8pwII299iMPOenYHetijO8y1H7wGg6GUcHiM6emz5KZQmETkTQU+n48zZ86Qm5uL3W6ntrY24yOmpRAdF5qbm/F4PFJUaLfbpbqk+J7ItBpTLIit9C0tLXg8Ho4ePUpxcTE2m23J7tRIJJK2kYE1K0wPPvggb37zm6VhrOeee44//elP/Pd//zclJSV88IMf5P7776e8vJzi4mLuuece9uzZo3hHHiy8obu7uxX/vrEQHTGJHWi9vb0UFRXR2tpKRUWFoh+kdKXyBEGQWr67urrIz89fseU72fNS8Rq7J7p5/NDdfPeKhf/eNwhf3ADXFf8Df/p0DT9v8nHVVVulJ9lzvnP8aQg+E4EsPWwthRcHwjg317P7QA+fEL7KX/zDCzz//FUUFf0/pqaOUlpRsfAaFEzlLUdWVhZ2ux2LxUJXVxdtbW10dHQA6u1JUloE8/PzF0WFok2OuHYD1I2YlE6pia+vqakJv9/PuXPnpOaQ+vp66ax0pfIikQiRSGRtCtPAwAC33XYbvb29lJSUsGXLFv77v/+bN77xjQA89thj6PV6br311kUDtqmgsLAwbak8MXqRNzRUVVWl1HQ0HcIUDAaZmJjg4MGDlJaWKro+ZClSFTE5Rzr5kA30OpgNw1fbIKyH/ym3QUBPfv5mSkv/HI06yhzodXpOjC2k84qyQI+e7WcuAPAG/og+EuJXv9rD7bcvCJNQ+ZaF16CCMIkYDAYsFgsNDQ309PRw9uxZXn75ZWw2Gw0NDSm9waUqOluq1Vw8R83h01S1p2dnZ2O327FarfT09ODxeOjo6JB+j+kSJvlAcTpJ6PRnnnlmxb/Py8vjqaee4qmnnkroouKhuLg4pd5cyyEIApFIBKfTyezsbFINDfEg1nvUSNdMTk7i9XoZGBggKyuLHTt2rLjNVClSFTHV6l6msHxhEeDdJ2A2AkQMMNKEwXDpniVTkYlv3fQtXum4m53lsLMM/r0rwiHHFOtPgIEIVjycO7eQol4QptsBmO0aobtbp+oKdtFN4uzZs9ITeardJFL9PpTvTvJ6vbS1tbF//36sVisNDQ0pT3elem5Kboo7NDSE2+3G5XKRnZ29Yg0qVYjdt+kWpjVr4ipSVFSkqjBFIhF6eno4cuQIMzMzFBQUcM0119DS0qLKEKT4IUmlqero6CinTp3i2LFjZGVl4XA4KCgoUEWUQPmI6fhxPY8+9T+M9H0FgO86wTnFgij95rvoJ0088cTckiKyMfdG6l9db7WrAgQ9/N0t8LPiBTF6P8/S3r4TgLk5Fy+8OL7wGrq62LixkB/+MD11gurqavbs2cOWLVsYGhpi7969tLe3Mzc3p9gZaj0gwcL7Xhxa3bRpE4ODg+zdu5e2tjZmZ2dTdq5aA71io8RVV13FVVddRTgcpqOjQ+p8VgsxUkvn9lpYgyau0RQXF8e0Xj1ZlmpoGBwcpKysTNUipVyYlAz1xZZvr9fL9PQ0JpOJ9evXk5OTw8DAQFo32CZC90Q3naOdPPK1GTyRf+F7t71AtgFeHoFf94BjdCffu/1fcVtzqKg4wQ03NF/yPX7yEz/jc7dx4+aF/zboYHspnBiD71a8kXcFDvF3fJc3Xq1DGCpkpnKK4s5301UM5sA0eZEp7r23kBtvjG+INBnkDQLyrrBUuEmo3YwgioS81dztdrNv3z7q6+ux2WyKe9alI6VWUlJCXl4ejY2NTE5OcuTIkRUbJZQkE1wf4DISplQ9uc3MzNDV1UVPT88lDQ2jo6Np6ZAD5SImeUu7uGCxvr5+USifjg22yQjTD878gHtfuJedZRHu/t9Ql78gKoIAX2sDdOAsO8rFrn5ef1UFgcCfo4juiW5ebm8jPPgbrNYfkJMTYj4CQ3NQnw97KuDUiI4ng98DoIphqv7rEf7jBiiuhP94B3xrK3zxN5VUnhjCFzbicumxWpP8oSSJ2JYdCARwuVzs37+furo6bDYbRqMxoe8p/o7UakaInmEqKyujrKyMyclJXC6X5Fmn5AByOrbXwoIgFhQU0NDQgMPhWNQoIW4PTsV1ZcIME1wGwlRSUpKSVN7ExAQ+n4+BgQEqKyvZtm3bJW/2dM0UKVGDEVu+fT7fqi3tart9J/qAEYlEOOU+xaf/56PcaRd4h3lBkESOjcDwvHgIPHvqx5SW3sDQxBBlE2W86HmRdvfdXFMB1VULX3ZyFAb+BN4tcKcDbqmDN7oEdO/v4UQFzJfDbJWO4vyFn8+NNfB4J3z6llFwZmGYWqhdqfU2WS2KkbtJuFwuDh48mPCKinRFTNEYjcZLWs1LSkqw2+1Jd8WmU5jEc3NycnA4HNhstkXbg0VHCSUzNloqTyFKSkoUyzELgsDIyAg+n4/x8XHq6urYtWvXspPa6Vyvnqgwzc/P4/f76e7upqCggHXr1q06nJnpEVM4HKa3txefz8epwCmKsgVuNS2IUujVVu+f+eE7LtkZAriy/5kDvn9mnRG+/N/QXAR/bVr4e/H4bWXAX//53+UZIO9aGFh0BYL0byLiZevD6Cs7eOJLlZhMAn5/nD+EFFNYWMjmzZtpampatKJCdJOIhXQI00pnyVvNfT4fp06dIj8/X3I1XwumsfJzo1Nq8kaJwcFBqVHCbDZjsVgUWayZCTNMcJkIUygUkhoREkHuYDA/P4/ZbGbTpk2rPokYDIa4TSiVIBGhmJmZwefz0dvbS1lZWVwt32quoRDPi0WYQqEQ3d3d+P1+6amyMb+RBy88yL+4I/imF9ZW/C8z/Ngn+4cC/HjXQopPZFNp9DX8+f9PhmCyv4aIroD6ejcDA2Z++cv7eOBTZVT8/ecI9fnY8m6YB4KvXrYeAy/+op4dzcFEfwwJEa9YiDdzu90urRIvKirCbrev+sCitjDFakckutKIrebt7e2LXM3jqaGks217uXPllk7j4+OSo0RNTc2SjhnxoKXyFEJ8uhsfH49bmMQbW1dXFwaDgYaGhkuW1q1EOlwYxHNjFYqJiQm8Xq9kGnvllVfGXVNQclFgLKwmTPJB34KCgksGmb9107e49w/3EhYWfjc/9C78O1vWTgpCJi7onkev+/Om2rAAR4YXBmgLsuDf/HB6HAIh6JyE6ZCBvze8xA+fzOO55yxUV3fRdF0J41xD75828P/h42vPZ/GRW4SFSEln4Fs3PcGO5vpU/6gUIzc3l5aWlkVuEnl5edjt9mV3KGVKKm855PNd8m3BFosl5hRYOhwYxFGUWO5DJSUlbNu2TVrOKKYxbTZbQjZV6TSPlbPmhcloNKLX66XUWyzMzs5KO4KMRiMtLS0J/RIzNZUntnz7fD7GxsaSnrFKR8S01Hni7627u3vFQd/bNt/GjdYbcY25mJqfwjnmZHf9bnbU7eBXF3/FB/7zeQbmoCZvoanh747DlWWwpxIG5+A7bghHAB0Q0fPm8JM8/Pc1fPAdOk64NlFXcBrK7+QNv9Hx+PZ8OAHzJz4Czo9DuZPvP2birzcr7wsZC8mKRXZ2Ng6HA6vVKg22dnR0LFlwz3RhEpFvCx4aGpKWMYqu5uJ2aCXPTAbxnhKPQMiXM/r9fs6cOUNOTo7kKBHra9AiJoXQ6/Xk5eURCARW/dpYGhriIVXr1VdjOWESBIHBwUG8Xi8zMzOYzWY2bNgQl93+cuelM2Kanp7G6/XS399PRUVFTKtCTEUmTEWmS/58l2kXAjo+f15gWyn84dVi0f8yL/zv8z/7JG/74ySfmv8njuWs4yMjL/DJfy8HIlDcxb/6TvPJVripBr7nFrj/lmm2Om3cH/gmBAwYpsxc1TqFWHdKF8mKRbSbhBht2Gw2zGYzBoMhLam8ZM6St8+PjY3hcrnYt2/fit2Ja0WYRMSUtugoITZKiL/L1aI/rV1cIXQ6Hfn5+csOocXb0BAPmZLKi0Qi9Pb24vf7CYfDNDY2UldXp9iTT7pqTKLzhJiG3LlzZ9JzKqYiE99+47e554V7+MPAwo11Swm0FIFOl0eucB8Pet7IJuAZ7uU976llx46F5hrnqJPyVzV+aH7BOSKsh0fK30UkYMBgEJYc1A2Hw7S1TXHuXAX19aNs2pQ6OyelHyDkBff+/n6cTqfkJqG2o7mSIlFaWsoVV1zB5OTkol1X0a7f6UhthcNhaQ4tUcTShNlslholnE4nZrMZq9W6bPYkHA6vGEGqxZoXJlgo4EZv94xEIgwMDODz+Zibm8NsNrNx48akowc56UzlhcPhRcX/7OzsVdeyJ3OemlP+MzMzTE1NcezYMelBQklXDTHVd6TnCCMjI6zP+glwhIqKd/Pt29sofPYcwaw83vvrv2LrdX/u+HSUOXjDwoJanvUs1KAMEfj0z/6G+4amsdsji0RJ/P38y79E6PmWm2NcycMP1/CJT3Tw0Y8WKm7wC6lLr+l0Ompra6mpqWFoaEgSKNHcV8nP1XKkInoxGo1s3rxZajUXXb/F5o90RUziWEiyyBslxsbGpIHk2tpabDbbJZmHeBefpoo1L0w6nY6CggJJmOQrGfR6vTSfk4qnnnSl8gD6+vpob2+nsLAwJS7mcsQPZiqFSayLeTweAoEAWVlZ7Nq1K+Flg6thKjLx1+v+Gp/vZQYHjwJQXf1hsh//7sIXvP2v2Hrd4g9tedYY9kKYj8C+oQVR+nbZ+9ixvhH48wNKKBTC7/cvdAue7uOWb/2Em3iRASq5Wfh/fP3r29i48Y80NupxOBwpn+ZXEnk6rKuri7Nnz0puEis9iStBKteq5+Xl0draKrWai6tE1F7lDqkzqS0tLWX79u1So8TLL79MaWnpouhXa35QkPz8fLxeL08++SSbN2+msLAw4YaGeFA7lTc9PS2lJAsLC9myZYsqRo/izzAVdSbRCsnj8TAzMyM5T/h8vpSJkpzp6R8BAsXFbyBfaCT7F78AIPi+913ytSMj/wZAyYiD33zPib3IQtneJ6S/DwaD+P1+urxeLKdPc+NvfkPekSPS31czxDGu5J8iH2Gq+9P8/ryeujonmzd34HA4lu1+iwc16z7FxcVkZ2dz5ZVXSm4SqbIGAnXWqsubP7q7u7lw4QJtbW3Sok81btqpFgd5o4Qowjk5OdhsNoLBoNb8oARnzpyhp6eHb3zjG7z+9a/nPe95D+WvrrdONWql8sS1GkNDQ1RXV1NWVkZFRYVq7sOpWDcQvf22sbFRskIaUmllRDgcYHp6QWyqqz9C1m9+gy4QIGKxEH796xd97UKtcuFrrT/op8YDM995kFBWFvPz8/h8Ps5eeBnd8f/HNf91BNuFhR1hQlYWPwq9m3/lb7iDZ3k3P+UenmT40z/mt/wl9/EY178ti//zfw7T2dkpLftL1/bSeBAj6JKSErZv3y7Va8QttIm4SayEmtGLuADT6/VSXV1NV1cXnZ2dkqt5KlOXakUt8nmv7u5unE4nc3NzWsSUDPv37+dLX/oSf/rTn6ioqOBd73oXTz/9tKrXkEpHBDG15fV6GR8fl1q+8/LyOHfunKopRHkqL1nC4TB9fX14vV50Op3UqCG/EatlgTQ8/GMEYQpopLj4JrJ/9FYAgn/zNxAlDFNTR5if96IP5VD5wiQRu52pt74Vb3s7I6dPc+rId3ms8WUGSyD8Dvjui/m8b/dHCN55J4E/WHjxo7m8ELmZ7/G3fJt72MAFbudfyWWOdz//U+6//1rq6rrp6OiQBCoRP7R0bq8V6zVNTU243W4OHz5MRUXFoiV/yZCOeo8gCFRWVtLS0sLw8LDUap7K1KXa6TRRhM1mM/v27cuIGlPCv+Uvf/nL7Ny5k6KiIqqrq3nb295GW1vboq+ZnZ3lrrvuoqKiAqPRyK233kp/f3/SFw3wyiuvsHPnTrxeLzt37lRl5UQ0YsSk5E1UEAT6+/s5duwY586do7S0VFqrIXbLpMMiCJIzjg2FQvh8Pg4dOkRXVxd2u51du3ZhMpkuudmoIUyCEGZg4Duv/tet6L0+svbuRdDpCL7nPZd8vRgtVR7UYZgD7/vex/nnnqPhwQewfvdvyXnTy/xoN9zTBBE9/J83zeO+/28R6uu57bYgL7zQwRe/eIBdD17LNk7yElcDMEs+oOPw4SwaGhp4/etfj8PhkFJjPp8vbXXM1VhOBPPz89mwYQPXXXcdRqORY8eO8fLLLzM0NJTU71WNVF40ohjqdDoqKyultRSzs7Ps37+fM2fOXNJ4lSzpqvOIn7s1ncrbu3cvd911Fzt37iQUCvHpT3+aN73pTZw/f17KL3/sYx/jd7/7Hb/4xS8oKSnh7rvv5u1vfzsHDhxI+sLvvfde6f8XFhYq/uaIBfHNo8RTqhhJiDciMbW11BtU7aYLsXU1kZuKVHdZxqVhufNS/frGx/+buTk3Ol0JgvBGsn/8YwDC11+P0Ni46GsFIcTo6K8AqPnPOWZLSyn608+xmC/gfi/MNMKWV7/2zbUL9kf9c2GO9Bzhr9ctGO3V1obYvHmY8vIIX/5yDvfzGDs5wn/yFkBgz56F16vX6zGZTNTX19PX1yd1v8W6jTadEVM0ubm5rFu3Drvdvsi7biU3iZVIRyOC3ExVRJ669Hg8HDx4kMrKSmw2myKRYTobENZ888Pvf//7Rf/97LPPUl1dzfHjx7n22msZHx/nmWee4bnnnuMNb3gDAN///vdZv349hw8fZvfu3clduQyj0UhPT49i3y9WxDfsUm/eWJHvecrOzsZqta7a8p0uV/N4zpybm8Pn89HT00NxcXHc3nypjpgGBv4JAKPxnUyM5UjCtFTTw+Dg/yMUGsQwDnNl4P/sGGPbxqR8g07I5f8NzNFsBGshfGY93HcS7vjPO5iYn+C2zbdJ32vnTnjve8P86Ec7OcpVgMB73xtm587FZ+p0Ourq6qitraW/v19KIYkCtdxTrZqD0LGKoNhQYLFY6Orq4sKFC3R0dGC32y9J4652XjoipuVu1EajkU2bNtHU1ITX6+XYsWOK7E1KlzgIgkAoFMqIVJ5iMZs44Co2Hhw/fpxgMMhNN90kfU1rayuNjY0cOnRIcWGamppS7PvFivjmSaT3f25uTrLXMRqNcbV86/V61c1jYxULsXOwr6+P8vJytm/fHncBPNXCND19homJfYCBwsL3Yvifw+i7uhBKSwn95V9KXzcxMUH3kcOExj8KFogUQvun/vx9inJ2UVH/fkpL38rZ87/iof338M87BDaXwOPb4KMnI3z0hY9yo/XGRed/73thPvShMIcO6dmzJ3KJKMmRzw8NDg7idDpxuVwrrkvPlIgpGvma9J6eHpxOpyRQsZirpqPGFMuZeXl5UmTo9/s5e/YsOTk5CTeyJPOgmwzig+eajpjkRCIR7rvvPq655ho2bdoELMzZ5OTkXNI5VlNTQ19fnxLHSqi9Xl1Ep9PFHUnI7XXKy8sTskXKxIhJdGkYGBhI2CxWJNXCJEZLZWVvJTu7nrJXo//gO94Br9pb9b34LFmefya0zsecZeHfCVmQN26k3PERymtvIzf3zym/2zbfhjHHyM/Pvp/3W2FTMTQboWMywtMnnuZDtg8tuoadO2Hnzth/h+KgZFVVFcPDwzidTtxuNxaLBYvFosqAazSJpg3lbhJyc1VRtJaLBiORiKr1j0gkEleUlp2djd1ux2Kx0N3dTWdnJ+3t7VitVsxmc8zXnq6ISXzYXdM1Jjl33XUXZ8+e5aWXXlLi28VNcXFxWoQJYp9lCgQCeL1ehoeHk7bXSYcwLScW4+PjeL1eRkZGqKurS8osdrWzlCAYHGBkZGFWqbr6I3hOTmLZt/C+HbzlBnp//l4iuheYts+ArNSkn4HN36zB8NNz6JYRgV31u7jjd/DWeijLgavKoWMSnjz+JG+teasi1y8W4SsrKxkZGcHpdOLxeGhsbMRqtWZUjWk15OnKwcFBKV25nNiqncoTP2Pxnil2uYmu5qIdkOhqvtpDRKoGbFdDtEK6LCKmu+++m9/+9rfs27cPs9ks/XltbS3z8/OMjY0tipr6+/uprVXWeTldEROs3Igg+vR5vV4mJiYWtXwnQ7ojJnkreyAQwGQysW7dOsUGYlMpTIODzyAI8xQWXsmvfnUNF+79Z1p2zuO+OZ+p3PcgOF79wjCUddUx31DMlL4N079D3l8/TGiFm4qpyMQ9Oz7K085v8en1CwL1Ex9EhAi+SR/lKDtfV15eTnl5OWNjYzidTvbt20d1dbWiZ6yEUs0I8mhwdHRUEluz2YzNZpM+L2qn8pJNbcmFd3h4GLfbjdvtjsmvTo3h8mhEA9dMcCFJWJgEQeCee+7h17/+NX/605+w2WyL/n7Hjh1kZ2fz4osvcuuttwLQ1taGz+djz549yV11FEVFRczMzKj6tCiy1JBtJBKRXL5Fn77NmzcrVlRMh3msaFcivq7p6WkaGhpiWqiYyFmpEKaucTd9fU+jA3S6W3Cfu493/vxfOFMOMANAgUdP1dTrKL3hEXQ7bJx6xQpA1YU6Ql9616pnfPiKD7PtxLdp8gv8Zx9EAIPOQKOxkclAajpHS0tL2bFjB4FAgIsXLxIKhTh37hx2uz2lYxRKf950Op0ktuPj45L7t+gmkS5hSvZMeZQrX+wn+tUVFRUt+vp0pvIyIY0HSQjTXXfdxXPPPce///u/U1RUJNWNSkpKyM/Pp6SkhA9+8IPcf//9lJeXU1xczD333MOePXsUbXwQz8yEVJ58xTcgDY8q/SZTO2KKRCKEw2Ha29sXtbKn6k2cioeLH575If9z/m7uaVpYtz45+Q/85f9ecLjLHoW8F4v51X9/mGu/8jHW37rgPN95/lkEfYgCL2T/708TikGATUUmvnHjt6VFhQadgSdueoLaglo66VT8dckpLi5m3bp1HD16lGAwyP79+6mrq8Nut685iyB5S7bL5eLAgQPk5OSo6nwtRoRKvh/FxX5TU1O43W4OHTp0yRByurfmrumISXRZuP766xf9+fe//33e//73A/DYY4+h1+u59dZbmZub4+abb+af/umfEr7Y5SgpKWF2dnb1L0wBBoOB+fl5PB4Pfr+f3Nxc7HY71dXVKfvQqiVM4joNr9fL/Pw8dXV1NDc3p/xDo3TE1D3Rzd+/eDe/e93Cf2fpF4xYjxy+mc2/nWD8iI07wj9AZ9Dzvo0Lu5R++MNsaqeeoOIqyP6fUp7dcTvvjfE8+aJCe6kdU5EJv9+v2OtZDb1ez7Zt2xbd1Gtra7Hb7Qk3pCyFGhkKo9HIli1baGpq4siRI3i9XqampnA4HCm35Epld1xhYSGbNm2SXM2PHz+O0WjEbrenbSfSZRExxXLjyMvL46mnnuKpp55K9JiYKCkpYWZmhnA4rOoPdm5ujtnZWTo6OiguLmbDhg2Ul5en/MOaamESHdp9Pp/UadTV1UVZWZkqHxhRmJS48UUiEY44F4xUe2ehNhe+54b/7IM7Cu7n1iNvJBzWLdql1N2t47mHDvHIvzkBeP7FO3nkx0ZueNPUJbuWlmO5RYWpRv4zk9/UXS4XBw8eVNTDTs3UeUFBAYWFhVgsFubn5zl69CglJSXY7faUOeur0YQgH0L2+/2cO3eOYDBIQUFBQpZUyaD2/XMlMuMqkkR8cgoEAqoYuE5NTeHz+ejv7ycrKwuTyURzc3PKzxVJlTAFg0G6urrw+/3k5+ezbt06yaG9p6dHtfSh3M080RtOJBKRPPn0s3qCET13HI2g1y1ESwadgQ+/s5F37e7iwIF+/vIvWyXRcTr1OP7iv8AAuR1g7+4njA6XS4/JpP7+rWQpKChg06ZNOBwOycOusrISh8OR1AZntWu6kUhEcsG22+14vV5OnTpFQUGBlKVQ8nrUrGmJD4BWq5V9+/YxODjIvn374m41T4ZMcX2Ay0SYxA9XqoVJ3hpdU1PDzp078Xg8qs+QKC1M8mHf4uJiNm3aRFlZ2aIPuZrr1ZNZsyHW+bxeLwaDAavVyq6aXXyr8lvc+4d7mY/8ue5jKjKRXz/C1q2jiyKhE7rv8xe3fQ2Ax4A3bv8hV5y6D7tdvYePRFlJLEQPO7vdjsfj4ciRI5SVleFwOBKy0lFbmOQ1rezsbJqamrBarXR1dXH+/HlpWFcpd/Z0DPTq9XoMBgOtra2Ew2Gp1byxsTHl82qXRSovk8jPzycrK4tAIKD49xYEgeHhYXw+n9TyvWfPHqmdMx0dckoJ08zMDD6fj97e3lWHfdVcr56IMIXDYbq7u6X0Y1NT06In6KXqPiLyc7onuvmT925etx4EAcZC8OFb4KBwF6b63wPpLwwni7gUTxSoY8eOUVJSgsPhiCsVnY6IKfo8uZtEd/eCO3tHRwc2my0mN4mVSJcDg5hSq6qqoqamhpGREVwuFx6PB5PJhNVqpaCgICXnahGTgohbbJUUpujV7A0NDUu2fKdjvXqywjQ1NSW5NFRVVcXk0pCpEZO4vtzn80nWMMstiFyq7hP9dc5RJ6+MQtsErCuC+5qhcwqmxw5heOEFwm96UxKvLPXEIxY5OTm0tLRgs9nwer2cOHECo9GIw+GIaclmOoRpOaHQ6/U0NDQscpNwOp3S/qREIoF0DrqK5+p0OioqKqioqCAQCEiu82IzS3SreTJoEVMKyHvVSiZZ4m35NhgMqvvWJSpMgUAAj8fDyMgItbW1XHXVVTE/eWVaxCSvh4kbORNtPJGf4yhzMB7Sc/eJCI9uge2l8KVNYA1D7kMPMf2GN0CGfHiXIpGHB3lazOfzcfr0afLz83E4HCvWbdKZylsOvV5PfX09dXV1i7wFE0mFpSOVB8tHLsXFxWzbto3p6Wmp1by8vFxqNVdiw4Ga7fgrkbmfsDgQI6ZkVl+IN7quri5yc3NxOBxUVVXF9EFQO2IyGAwxd60JgsDY2Bgej4dAIHBJKjJWMiVimp+fl9ZoFBUVLVkPS+QsEVORiW/dtFCP+odzYf7pCjDnw8inDTR89CLZP/oRwVfHITKVRH8WWVlZ2O12Ghsb8fv9nD9/ns7OzmXXvqu9hiKe8+RuEvJUWENDA1arNaYbcLoWE64WqRUUFLBx40bJ1fyVV16hsLAw6QYQrSsvBRQUFDAxMRH3v5udncXv90vrGeJ98lZ7NxL8eRJ9pTewWBvzeDxMT09jNpvZuHFjwsXTdEdM8gaNkpIStm7dqtgcS7QAyutRjQU6Rv3vIdA6RtsnYN0Xv0Dw1lshzhSKWqKuxDlZWVnYbDYaGxvp6uri4sWLdHR04HA4FjUWZGLEFI08FTY2Nia5SZhMJmw224oZg3TUXMSH3FjOzc3NpaWlRWo1P3/+PO3t7dhstoRazbVUXgqIV5jkdZaKioqE1jNA+pofYGlhEgSBgYEBaShWzLsn+4ZLV8Q0Ozsr7XVKdI1GLGdFI69HlWX/gI6OtzPwxjCF3kFqn3iC+Yceiun7RyIRuru7cbvdCILAwYMH2bx5s6K1gWiUEguDwYDFYqGhoUFqLJCvfU+HqWoy55WWlnLFFVcwMTERU60mHRFTPMIkIj5IWCwWenp6cLvddHR0xF1f05ofUkCswjQ2NobP55NavuOpsyxFOpofllp1Lp/bEQRBcTukdBjHOp1OhoaGqKioYMeOHSm7ma8muMXFN9DY+HV8vo/h/lvIf+RxCno+gFBfv+y/iUQidHV14fF4CIVCFBUVUVlZSVdXFwcOHJCWJyr9mlIRxcgbC3p7e6U1FUajUXWLICWEoqioiK1bt9Lc3CzVaiorK7Hb7Yui8HQKU7LrRAYGBqQGELG+tlr6PlOWBMJlJEyFhYXLLgsU01per5fJyUlF3bDTlcoTU2vhcFhyaRDndlbbgJsIaqXypqen8Xg8wMKHNJn1ILEQ6w2gquqDzM60MTD4HS7+/TybvvMxsj//s0u+LhKJ4Pf78Xq9kiBt3LhREiCHw0FXVxdOp1MSqE2bNikaBaaK6LXv58+fZ3R0FKPRGNPa92RRWnTFWo3D4cDj8XD06FFKS0ux2+2Ul5enbTFhsn51Op2OmpoaqqurGR0dxeVysXfv3hXTl4IgaBFTKjAajZc0P0QiEfr7+/H5fASDQcxmM1u2bFH0qSAdqTxYePP5/X76+/vJy8ujpaUlphbfREn11lxx0eDg4KAkrA6HI6WiJBJritLc8CXmBo4znneUi9f8F2VHf46rphZHmYO6wrpFgiTWK6MjIp1OR0NDA2azWVomd/DgQcUESo26j7jOYWxsjOnpaXp6ehZt1U1VnSJVQiGf6/L5fJw8eZKCggJyc3NV71JTUhzkbu2BQEByNa+pqVnSlkprfkgBhYWFUipPHkXo9XoaGxupra1NydOA2qm8+fl5fD4fkUiE8fFxNm7cqEir6GqkKmKamJjA4/EwPDxMXV0du3btIj8/n8HBQVVqWvH83HS6LGzbf0XbixuYqZxg/8Tf8snnYSKk5y7LXdxYduOygrTUuWLapaenh46ODg4ePCh1GiZjFaQmhYWFXHHFFQwNDdHZ2bloq66SD4BiF2oqI5icnBypbd7v99PR0YHBYKC0tFQxN4nVSFXUUlxcLKUvPR4Phw8fpry8HJvNJrnlaKm8FFBUVITP5+Ohhx7ihhtuoLi4mObmZqqqqlJ601YrlSd3aSgrKyM7O5uWlhbVbmBKNz+IT3Cjo6NLtrCner26nHjOMRhKMNY/Sd/E7bQUwfeuhP99OMKTnqe44/V3sK5uXVxn63Q6KT3W09NDZ2cnhw4dSlig1N5gK6aVq6qqpK26nZ2di7bqKmGjI/6O1HhtYjPB5OQkc3NzkpuE3W7HZDKlVKBSnU4TZ/7EVnMxOrRarWkbKF6Ky0KYvF4vBw8e5MSJE4yPj/Oe97yH9evXq/ImTnUqT949WFlZKTUBHDp0SNVITamIaWxsDLfbLW2+Xb9+/ZI3LrWEKd73SDgc5sjJ/6Hs1U23FTmwswyOjEY41zsQtzDJr2MpgTIajWzevDkjI6hoEZS3Zotr3/fu3UtDQwM2my2pmq5SS/viQRAEysrKuOKKKyQ3ic7OzqTcJFZDrTpPTk4Ozc3N2Gw2aSQA0FJ5SuB0Ovnc5z7Hz372M1paWmhpaeGPf/yjqtcgRkxKP6kGAgG8Xi/Dw8NLdg+q3SWXTMQkrmL3eDxMTEzEtPk20yKmUChE18WLRH7/d9hefxayYGAWHuuAI6NAxIAw7Fj1+6yGXKB6e3vp6OiQBGrTpk2rzm6pHTEtd1b02ne5QCVSt0mHMIkiIXeTELvdXC4XFouFxsZGRY1V1fbnE70Gq6qq2L9/f8ak8pL+Cezbt49bbrmF+vp6dDodzz///KK/FwSBhx9+mLq6OvLz87npppvo6OhI9lhgoWBeUFDAuXPn+NCHPpSWzYtyF4ZkEW/gJ0+e5JVXXiEvL4/du3ezfv36Szpp1BamRCImQRAYGhri+PHjnD17lrKyMq6++mrsdvuqH4BMiZhCoRCHzx/mJ0/ew/Sfrmf0hgVR8pyo4PYjeg6PABEDut99h90blNu/pNPpqK+v59prr2XTpk3Mz89z+PBhXnrpJcbGxhQ7JxlicWIQ177v3r2b2dlZ9u3bx7lz5+LeOC2+99LpzSd2u+3evZutW7cyMjLC3r17aWtrU2xRabo648TXmgnba0GBiGlqaoqtW7fygQ98gLe//e2X/P2jjz7Kt771LX7wgx9gs9n47Gc/y80338z58+eT7njZunUr3/nOd4CF4t7MzExS3y8RxDduMk868nb2qakpzGYzGzZsWPFJLJMjJlGQPB4Ps7OzNDY2xj3km+6I6eULPn7x4mlco3+ir+afeWAHzBSAEAZr8INcyHuS+c8PQqkT/ZiDJ79UnZJdTWIHXG1tLX19fbS3t3P48GEKCwvZvHnzJRFUpkRM0RQXF7N9+3ZpuPWll16Ka+27mjUmkeW6AHU6HZWVlVRWVsbtJhHLma/17bWggDC9+c1v5s1vfvOSfycIAo8//jgPPfQQb33rWwH44Q9/SE1NDc8//zzvete7kj1eori4mOnpadVtUsQ3USIiIXdpmJubo7Gxka1bt8b0BsnEiEl8PR6Ph2AwiMViob6+PqEPWroiplAoxO2P/wu/4UHqCiL8n53w+krQ6WB4Dr5wQc8v3v0pbr82wk03VeJ0VuFwCClfICgXqP7+/kUCtXHjRqmzSi0xF8+K92FMHG6dmpqS1r7X1NTgcDhWdLgXozM1P9uxPGxGu0m89NJLSa2xT1fElEkzTJDiGpPb7aavr4+bbrpJ+rOSkhJ27drFoUOHFBWmoqIixcLpeNDpdHE3QMhdGiKRCBaLJW6XBrUHe1eKmMR5sejXk0yuXO2IKRQK4fV6efHoeU4VPMBnrAI3VIPh1fvgVAg+cxbaJiM4R50LlkUmIeZV60qh0+mora2lpqZGEqgjR45IAiV+jRok8xAoRnziVt2DBw9SVVWFw+FYcpZLbfsjiC96kQuu/PXY7fa4GlfSJRBixHTZpPJWoq+vD4CamppFf15TUyP9nVKUlZXFnbdWiliFSUmXhkyImCKRiLQtVqfTYbFYFJv3UEuYwuEwgiDw0ksvEYlEcI1PYyoUuOnVt+zLI/CfvXBgGMLCwkp2R1nyTQ7JspxA5eTkkJubq0rmQIkz5O4L4tr3iooKHA5H2u2BEjmzsLCQTZs20dTUhNvt5siRI5SWlkpbglf7eWkR0wKZk1RMkpKSEkKhELOzs+Tn56t69mrRSzAYpLu7G7/fT25uriLzVemsMUWvLxft9pW8caRamMQIye/3Awuvb/PmzZRbgzz9Kx0/8wu80A/OKUAAdKDXGfj2m759ybLBdCIKVFVVFW1tbXR3dzM/P8/+/fuTXgmyGkqKX15eHuvXr5e26or2QE1NTZSVla0ZYRIRX4/D4ZBWUxiNRux2+4qf/XA4rIhVWrxkkusDpFiYamtrAejv76eurk768/7+frZt26boWeLTVSAQUF2YlouYoncHJbPMbqkz1Y6YwuEwPp8Pn88nzUGkaoA5VcIUCoXweDx0dXURiUQoLy+nubmZvr4+zpw5Q0VFBe8oeILvdH4M9GGIGOAPX+a+/72Vj7zLllGiBH9OC7tcLnQ6HZs2bcJgMNDW1saRI0ekiESp952cVERlubm5rFu3DpvNhsfj4fjx4xQVFV2SdVEDJVq35fNCPp+Ps2fPkpubi91up7a29pKfX7pTeZlCSq/EZrNRW1vLiy++KAlRIBDg5Zdf5sMf/rCiZ4l56fHxcdXfxNG2RPJVDWVlZYruDhJRU5hCoRADAwPMzs7S399Pa2srFRUVKU0VKS1MoVAIt9tNd3e3JEgbN26UOkOLi4uxWCx4PB7eu8HKm2b/g4sDEcwFrfx/z5pUryWthiAI9PX14XQ6AaRdSeLvpKqqiqGhIS5evMjRo0eliX8lf2+pTBdGr33v6OhAEAQGBwdT6gkpR8kOOXEJo8Viobu7m7a2Njo6OrDZbIvcJNSeYxK57FJ5k5OTdHZ2Sv/tdrs5efIk5eXlNDY2ct999/HFL35Remr47Gc/S319PW9729uSPXoRBoOB/Pz8hJYFKnF2JBJhamoKn89Hf3//IpeGVKCGMAWDQSniy8vLIysriyuvvFKVm4JSwrSaIMkRn9ZFgarO66aqaoiSkmIg/g6rVCAIAv39/bhcLsLhMHa7fclGE7lNkChQx44dIz8/n40bNyoiUGrUscS170ajkfPnz3PmzBny8vJWXfuuBKlIHxoMBhobGzGbzYtWiNhsNsxmsxYxvUrSV3Ls2DFuuOEG6b/vv/9+AG6//XaeffZZPvnJTzI1NcXf/d3fMTY2xute9zp+//vfK+7aq9PpyMvLY3x8XNHvGwuRSASfz8fU1BQ1NTUpX9UAC8IUDAZT8r3lKUhxb5DBYODUqVOqde0kK0yhUAiXy0VPTw+RSISKigo2bNgQ0/tOdJu2Wq243W5efvllqqurY565SQViK77T6SQcDse8pXQlgdqwYUNS0Yeaoxl6vZ7c3Fx2794tbWuVb9VNxXWksq4lXyEiPmg4nc60RS2hUEh1J/WVSFqYrr/++hVvIDqdjs9//vN8/vOfT/aoVVE7YhobG8Pr9TIxMUFpaSm7d+9W7Zebiohpbm4On89Hd3c3ZWVlbNu2TWp1nZqaUr2mlYgwJSNI0YgFbFGgDh8+TG1tbdJDlPEgpq+cTifBYPCS1E+syAVqeHiYixcvcvz48aQEKhbnB6UQRULsZhW36ra1tUlbdZMdUYg+T40WdXl35fDwMCdOnODChQtMT09jtVpVa4TIJANXuIy68nQ6Xdzr1RNBEARGRkbweDxMTk5iNpvR6XSUlJSo+sShpDDNzs7i9Xrp7e2lvLycK6644pJZEjXnihI5TxSk7u5uBEGgsrKS9evXK/I7EW/eVqsVl8vFoUOHqKurw2azpazRRnTPcDqdzM3NSYKU7M1DdC245pprGBkZ4cKFCwkLlJoRU3T0Ik+J9fT04HQ6JYFSwgFc/GypdbMWfy95eXmYzWbJ7shsNqf0fSZy2aXyMolUCpP45OrxeJibm6OhoUFaOnjhwgXVlwUqIUwzMzN4vV76+vqorKzkyiuvXHZaXTxPrZtRrMIUCoVwOp309PQoLkjRFBQUsGnTJmw2Gy6Xi4MHD1JfX5+wMelSiA8+TqeT6elpqfag9A1SdAKPFqi8vDw2bNgQc7elWoX65aIzcZ24uFXX6XTidDqT/rmlwzQWFpoQSktLsdlsBAIBXC4X+/fvT8pNItZzNWFKEakQJrmrQTgcprGx8RKbnXStV0/0TPkqjerq6phqYuJNIVOEKVqQqqqqaG1tVSVqFV0LJicnJVsd0SctmdSLKEhTU1OSc3Wqn9jlAjU6Osr58+clA+H169ev2GCgZipvtbSa3AG8v78fp9MpbdVNZEVFOoVJ/J0XFxezbdu2pN0kYkGLmFJIYWEhU1NTinyv6CFScQvuUm/UdKxXT0SYJicn8Xg8DA0NLblKY7XzQD0vtuWEKRQK0dnZSW9vryRIqxnepgqj0ciWLVsW+aSZzea4awOjo6M4nU4mJiawWCxs375d9ZuEuIZbFKgLFy5w4sSJFQUqnam85ZDXbAYHB+ns7MTlcmGz2eJa+x4Oh1X35hPPjX6dS7lJlJWVYbfbJY9EJc7VhClFFBYWMjk5mdT3CIVCdHV1xeXSYDAYmJ+fT+rceIlHmKLXlyfSpCG+frWKpNHCFAqF6OjooK+vD0EQqK6uXnbJoNqIPmli6uWll16isbERi8Wy4vWJu4oCgYBk4JvufThygRJTfCdOnCA3N5cNGzYsEii1hSmes3Q6HdXV1dI8l9PpjGvtezqaAQRBIBKJLCsQcjcJj8fDK6+8QlFREXa7PenuSk2YUkgyEZPYIt3d3U1hYWFcLg2ZmsobHx/H4/EwOjqKyWS6ZH15vOeB+hFTJgtSNGLqZXx8HKfTuUig5DdC8e/HxsZobGyUapWZxlIRVG5uLuvXr6empiYta9zjJXrtu1ygVlr7ni4LJFg9fSgfPvb7/Zw5c4bc3FwcDgc1NTUJ/U60VF4KKSoqor+/P65/I3dpKC0tZcuWLXG7NGRaKk/cFrva+vJ4kEdMahCJRKRmk0wXpGhKSkq44oorpIhIFKiysjK8Xi+jo6PSFt+18HrEBY9jY2OcP3+ekydPkpubq+p7Ptl6lnztu5g6FbfqWq3WSzII6RAm8ecZa6SWnZ0tuUmI69Hb29ux2+0xzbiJaBFTijEajbhcrpi+dnp6Gq/XS39/PxUVFUu2SMdKtCWRGkRHaeL2W7fbLS0bXG19eTyI+fZUR0zBYJCOjg4GBgaAhZv81q1b02JsmSzi9tbu7m46OjpwuVyUlJSwe/du1eaglKS0tJSrr76aoaEhzp49SygU4sSJE2zYsCFlQ64iSs4UlZWVceWVV0qR6759+y5py06HNZB4D4n3XIPBgMVioaGhgZ6eHtxuN52dnVLjx2pCJ3bbasKUIoxG46qrLyYmJvB6vQwNDcXckbYa6UzlidtvPR4PMzMzNDQ0xLxsMF4SWa8eK6IgiRFvdXU1RUVF+Hw+Tp06RVNTk2KFXrUQu/YGBwcxmUyUlZXh8/k4cuSIdCPJpJvBaoTDYfx+Px6Ph8LCQmpra+nq6uLUqVNcvHiRdevWUVdXt2ZcGMTIVt6WXV9fj91uT0uNSezIS/TnJ7bOm0wmyU3C5XJJHZ7LPaSKgphJ78XMuRIFKCoqWrbGJK+31NXVsWvXLsWG1tKRyhPdvo8ePcr8/LzUxp7KN1c869VjJRgM0t7eLkVINTU1rFu3TkpxNTY2SuJUXFxMU1OT4q2ySiNuZx0YGKC+vp5rrrlGShVVV1dLtQ6v1xvzU206iUQidHd343a7ycnJYdOmTZLXXkNDA2NjY1y4cIHTp0/T1tZGS0sL9fX1igpUKlvTxdqg+CDx0ksvUVJSoupAOShnpCrvTBwaGsLlcuF2u6V6Z3T2Id4UohpcdsI0MzMj/bc4rCjaBpnN5pTUKdRM5Ykmnm63m0gkQl1dXcLry+NFyYhpfn5+UcouWpBERFfmhoYGaQ1CeXk5DocjZQa5iTI9PY3L5ZLWvFx99dWXPPyItY7y8nKGh4clgVLK2UFJBEGQjEb1ej3r1q1bsm28tLSUPXv2MD4+LhmttrW1sW7dOsUEShCElP9sxPZ/h8PB2bNnmZyc5OTJk6q915Q2cBUbP6qqqhgZGcHlci2qq4nvzUzbXguXmTCVlJQwPT1NKBRicHCQrq4uZmdnaWhoYPPmzSnrfFIjlScO+orNACaTCafTqYj9SqwoETHNz8/T3t7O4OAgsLwgRZOdnU1zczONjY14PB6OHDkireJOl7GqyMzMDC6Xi76+Pmpra9mzZ8+qNSTRgqaiokLywvN4PAl74SmJ3DA2EolIPnSr3bhKSkrYs2cPgUCAc+fOSQLV0tKCyWRK6sYXiURU61wsLCykvr4eQRDIzs7m0KFDKRtslZNKZ/Hy8nLKy8sXpS3r6uqw2+2EQqGMeiCCy0yY8vPzGR0dZdu2bbz73e/mtttuUyWaSGUqL3p9ubiOPRwO43Q6VfevS1SARUEaGBiQUg0tLS1xR6/y1RQul0syVrXb7aoviJydncXlctHb20tNTQ27d++OWyTl8zYDAwO4XC5JoOLprFICuT/f/Px83N1dIsXFxYsE6uzZs1IElahAqd0lF4lEyMnJWbT2/ciRI1K0rvR+NVCn4UKetnS73Rw4cIDi4uK0PggtxWUhTDMzM3z/+9/nC1/4AtPT03zqU5/iQx/6kGo3qlRETOFwmJ6eHnw+n5TOks8oiIKkZpE2kYhpfn6etrY2BgcH0el01NXV0dramnQtTPR0EwXq4MGDitgCxcLs7Cwej4fu7m6qqqrYtWtX0h5mOp2OmpoaqqurpcK12+1W3DV7OeR2SEr588kF6vz585JAtbS0SObHsaKm/ZF4nvgzX27tu8PhULQhR83PstFoZPPmzTQ1NXHu3DmCweBrJ5X31FNP8bWvfY2+vj62bt3Kt7/9ba666ipFzxgZGWHjxo1UV1dz//3386lPfYp77rlH1dBUvGEr8VQXCoUkQVppfbl4jprdgPG4TaRKkKIRfesmJiYWzQxZrVbFUz9zc3PSWvbKykpFBCkaeeFaXJkuCtRylljJMD4+TmdnJ4FAIGV2SMXFxezevVsSqHPnztHe3h6XQKmxgkLOUp9l+dp3r9crOS84HA5FFi+mY0lgfn4+NTU19Pb2qnruaqRMmH72s59x//33853vfIddu3bx+OOPc/PNN9PW1kZ1dbVi55SXl/OrX/2K3bt309XVxSc/+UkmJydV7dwS30zJhOJyK6T8/PxV15eLc0VqdgPGMscULUj19fWsW7cu5a2oRUVFkutCZ2cnL730ktQmm+zZ8/PzeDwe/H4/FRUVXHXVVSkvhotivpxAJXsTFIV8ZGRkkVN+KhEFamJignPnzkkC1dzcTENDw4qvSe1U3koiIT4wWq1WqWO0oKAAh8MRsyt7vGemkkxbqw4pFKZvfvOb3Hnnndxxxx0AfOc73+F3v/sd//f//l8eeOABRc/as2cPsDA4BwtPgWoKUzLRi3x9eWFhIRs3bqSsrCzmlQOZEjHNz89z8eJFhoaG0Ol0mEwmWlpaVJ+NKCkpYceOHVJqyufzJdySPT8/j9frxe/3U1ZWxs6dOxMewk4U0TW7traWnp4eOjs7cbvdCa8Wn56exul0MjAwgMlk4pprrlF9eLmoqIjdu3czOTnJuXPnpG20TU1NNDY2Lvma0pnKW47s7GwcDgcWiwW/38/Zs2fJy8u7JO0eK+kUpkyaYYIUCdP8/DzHjx/nwQcflP5Mr9dz0003cejQoVQcCSyEpQaDQdUttrDw2uKNXubn56VtseL6clFY4zlX7a2y0edliiBFU15eTllZGcPDw3R2duLz+WLueAsGg3i9Xnw+n+TekO7ZKfneIXFzq8vlivkpXd6osVwru9oYjUZ27dolCdSFCxfo6OiQui/lrykdqbxYRSIrK0tyL/f7/Vy4cGHRVt1YBSpdwpRpPnmQImEaGhoiHA5TU1Oz6M9ramq4ePFiKo4EFj68+fn5BAKBlJ2xHLE2QKy0vjxe0hExiam8ubk52traJEEym800Nzdn1Btc3pIttj97vd5lbxihUAifz4fX66W4uJjt27fH/bCQavR6PQ0NDZJAXbhwQRKopRym5+bmcLvddHd3U11dnVDnYKpZTqCampqwWCzSA5Haqbx4U5vi2vfGxka6urpob2+ns7MTh8MRUwNLOmyQxHMz6XMLl0lXnpx0CdNqLeMzMzP4fD56e3upqKhgx44dSdcp1LZC0ul0zM3NcerUKYaHh6Wn+EwTpGjkHW+9vb3SzJCYDpNb7RiNRrZu3Zrx9kfijjCTyURXVxfnz58nLy9PKsSHQiGpLlZeXq5KXSxZRIGampri3LlzXLx4kc7OTpqamtKSyks0etHr9QmtfQ+Hw2nxhAyFQhlnJpySu0llZSUGg+ESp+/+/n5qa2tTcSSwcANK5Xr1lVjO/UFuFrva+vJ4UTNimp2dJRAISILU0NBAU1NTRgtSNGIzRm1trRRttLW1EQ6HKSwsZMuWLTGvOskURANPs9ksrUAwGAyEQiGp3pbuNGS8FBYWctVVVy0SKIDBwcGE1zrEixIRWvTad5fLteLady1i+jMpuZqcnBx27NjBiy++yNve9jZg4Rf94osvcvfdd6fiSIn8/Py0CZNcJBJZXx4vagjT7OwsFy9eZGRkBJ1Oh16vp7CwkJqamox7M8eK2NofPROWjo2lSiJev9g9GYlEVPdwVJK8vDxqamqk5Z/d3d309/djt9ux2Wwp/V0pmTqMde17Ooxj4TUkTAD3338/t99+O1deeSVXXXUVjz/+OFNTU1KXXqooLCxMizCJqTz5+vLa2lpFzWKXOjNVwiQXJDE14XA4APD5fJw8eZKSkhKam5szPkUkEolE6Orqwu12SxtZKysrCYfDeL3eNWUUKxKJROjp6cHlcklOBZWVlVK9THxNqXIrSAWi24nL5SI7O5stW7ZQUVHBzMyM1GLudDpxOBxYrdaURBmpiF6i177LBcpisWjNDzJSdjXvfOc7GRwc5OGHH6avr49t27bx+9///pKGCKUpKChIer16IgiCgNfrZXp6mvr6+oTWl8dLKoRpdnaWCxcuMDo6ukiQ5G9cu92O2WyWbFqqq6txOBwZu2NI7o6dnZ3N+vXrF3WyZWVl4XA4aGhowOv1cuzYMSoqKmhqalJ8gFYpBEGgr68Pp9O5pMGq2Mrc2NgoDYOKbgWZKrqCIDA4OEhnZyeRSISmpqZFM1sFBQXs3LmT6enpRQJlt9uxWq2K3tRTGb3IbahEI1+3201WVpaiM56xkokRk05Q29s9xfzFX/wFzc3NfOlLX1LlPHGdxvDwMGVlZWzYsEG1Aubp06cpLy/HbDYn/b2iBclsNl8iSEshNzBVyxIoVuRP3gaDIebZn+hOtkwSXbnBajgcxuFwxOQIET2T5XA4VJ/JWomRkRE6OzuZmZlZtUlARIygRkdHMRgMigrUwYMHpVXlajAyMsLx48cRBEFa+67G50gQBPbv38+WLVtUe62xkFkyqQBGozHlEZMgCIyNjUnry0VblZKSElVvykpETNGCZLFYsNvtMT9B5efns3HjRiwWC06nkwMHDkh7X9Ryg44mEolIxWadTnfJk/dq5Obm0traKvnwHTp0SHJiTnUUvBziQsjOzk7m5uZivnmLiG4FFotF8nurqKhI+/qQQCBAZ2cn4+PjUqt1rMKSn5/PlVdeKQmUuCVYCYFSuz29vLycvLw8GhoaGB4eXrRVN9XvuddUKi9dFBYWLrssMFnE/U4ej4epqSkaGhqk9eUXLlxQvdCcjKv5zMwMFy9elATJarVis9kSfoOKbdZjY2N0dnbS1dWl+hI8Mb3lcrkQBCFpfzlRdK1WqyS64s1Czfba0dFROjs7mZqaSvpnmpOTQ0tLiyRQR44cobKyEofDoWracnp6ms7OTgYHB6XPUaI/U7lAiS4SLpcLm82GzWZL6GeVrtXqJSUlWK3WFde+p+JcTZhSjNFoZGhoSNHvKa4D8Hg80n6n6PXl6VyvHg/T09NcvHiRsbExRQQpGtEpQe64EOuAYaKIyxNdLhfhcFhxR26xlXxiYkLy4VMjKhSjibGxMcUNVuXrQzweDy+//LIq+63m5uZwuVz09PRQW1u7aLtvsuTn57Njxw5JoET7JqvVit1uj0ug0rVaXXzPimvfRU9D+dp3JdPKgiBowqQGK61XjxexGOvxeKT15cttGU3HevV4hClakGw2G1arNSVvSLnjQn9/P52dnXi9XpqampIyuYxGrLe4XC6CwWDC+4NipaioiO3btzM2NiY5mStlFCtncnISp9PJ0NAQjY2NSUUTq5GXlyelLd1uN4cPH6ampkbxG6Bo8+T1eqmsrEypA0W0QIkD1RaLBYfDEZPgqJ3KE8+MvjbRnFi+9l3cP6ZEhCveszRhSjHR69UTIRKJMDAwgMfjIRwOY7FYqKurW/ENbTAYCAaDSZ0bL7GIoVyQDAZDSgUpGrE9trq6Whpo9Xg8NDU1JeWsIF9ol0i9JVnEqFAs2Is+fMnuMJKvZjeZTLzuda9TrWaZn5/Phg0bsNlsUl1NiQWMoquG2+2mqKiIK6+8UrWuQLlAidZNHo8Hq9W6qkCpLUxi5LLcNYlr35uamqT9Y2JjTjI1QvH+8ZpxF08XyURMYtHc6/UiCAJWqzXmGsVyzg+pRK/XLyuG09PTXLhwgfHxcaljyWKxpOXJSO7vJs7WlJSU0NTUFFdnmNgA4HQ6mZ2dxWq1KrLQLlHKy8vZuXMnQ0NDUlSYSNQmN1itra1Nq8GqWFcTBergwYPU1dXFXeOInq9Kp6tGfn4+V1xxBXNzc5w7d25VgRJFQm3TWFhdIAoKCti0aZO0VffQoUNSjTARwQ+FQuj1ek2YUk0iEZN8fblYd6mpqYnrjZkpqbxMEqRoxIjNZDJJnWFVVVU0NTWtmDYSm06cTifT09OqN1WshE6no6qqisrKSmmqX/ThW60TcH5+HrfbTVdXF1VVVRllsCreAOUCVV9fv2qXmJhe7ezsRBAEWlpaVLMRWo3c3NwlBcpisdDU1CS9n8QJGjXfX/FGLmKEK27VPXLkiDQGEI/xcCgUyojPUTTpv1spTElJCdPT0wiCsOqHQVxf7vV6pYHERHbcQPqbH6amprhw4QKBQECa2bFYLBn5phM7wxobG3E6nRw6dEgq7EanruQrvy0WyyILl0xCnrbs7e2VCu9L1dXkazXKysoy2mBV3BAs1jgOHDiw7LzayMgIHR0dzM7O4nA4UlrvSwa5QJ0/fx63243X65UEShQmtd3MEzlTrBHa7XbcbjfHjh2jpKREWvu+2r0sE1vF4TIUpuLi4lUjplAoRHd3Nz6fj7y8PNatW7fkyoB4SFfENDc3x7FjxxYJUqpsWpQmLy9PaseWd7tZrVapAWBiYiJlK79TgV6vx2QyUVdXJ9XVxMV+paWlkot5cXExV1xxxZqxCRJrHBMTE1IRvqGhAavVyuzsLB0dHQQCgbhnkdJJbm4u27dvv0SgxIH1dGzMTfQelJOTs2jt+4kTJ2Ja+57suaki8z/pcVJSUrKsMAWDQWl9eUFBAevXr1/xlxYPakdMExMT+Hw+ZmdnMRgM0u6atSBI0RQWFrJ161bGx8e5cOECXq8XnU5HY2MjW7duTdugbjLI62qiD58gCBQUFEjeb2uRoqIitm7dSiAQoL29nX379gFgNptVWc+eCuQCdeHCBXw+HwDt7e20tLSoIrJK+eTFu/Y9E1vF4TIVpvn5eebn56VUg3x9udFoZNOmTTGvL48VtZofJiYmaGtrkyKkkpISJiYmmJmZIRgMZowdULyMj4/jcrmYnp6murqaqakpent7yc/Pz9iU0GqItcvu7m7y8/MxGo0MDg5KqeNMsgSKh9nZWbq6uhgfH6eyspJgMEhPTw9ZWVlpdfxIlsnJSWZnZ8nJySEvL0+ycGpoaEi5QCndbLHU2vfc3FzJZkm892k1JpUQUyOBQICSkhJpW2xJSQlbtmxJWeok1am8iYkJLl68yMTEBFlZWYsipKmpKTo7OyU7ILXawZVAHCAcHh6moaGBjRs3kpOTIw3NymegEq3/qY3cYFWn09Hc3CzdDObn56XGj3Q4LiRDMBjE4/Hg8/kuadaQz3Y1NjbS2Ni4ZgRqYmKCjo4OxsfHsdlsUmPN3NwcFy9elATKbDazbt26lNzIU+UsLl/73tXVJS1fFF1RMjViuuxMXMVI6ZZbbqG8vJw777wTi8WS8tmJyclJXnnlFa699lpFv2+0IIlvsqWersbGxujo6GBqagqHw6HqbE+8yIdIzWbzsqaVYtux0+kkLy+PpqamjE2DiQPZTqeTUCi0ogPF7Owsbrebnp6elAy0Kkk4HMbn80m1sZXWgoyOji6qDSo9fKwks7OzdHZ20t/fv6LV1Pz8PBcuXGBgYACdTkdDQ4PiAiV2dF599dWKfc+lEJ32RR/JwsJCsrOz2blzZ0rPjZfLSpjcbjdf+cpX+Od//md27tzJF7/4Rfbs2aPK2dPT07z88svccMMNiny/QCBAW1ubJEh2u52GhoZVhUa+OkAQhIyLNMTursHBQUwmE1arNSZLmuibY3Nzc8akwsR29s7OTmZnZ6Vh21geCuTu7Ok2io0mehapubk5pocCQRAkf79Ma++HxZFfdXU1TU1NMc1oBYNBzp8/nxKB6unpwe/3s2vXrqS/VyyIaea2tjaqqqq48sorVTk3Vi4bYbrrrrt45plnuPXWW/nNb37Dj3/8Y66//nrVzp+bm+PAgQNcf/31SUUpgUCAixcvMjk5GZcgRSOPNPLz82lubo5rvkFppqamcLlcDAwMxDQPsxzR6aRUe7uthmhaOzExkVRH2tTUFE6nk8HBwbQYxcqRzyIBCT/cyAV7ZmZGEXeMZIhEIotcKBJ9uAkGg1y4cIH+/n50Oh1ms5nW1takXpff76e/v191gTh79iw5OTls3rxZ1XNXI+XC9Mgjj/C73/2OkydPkpOTw9jY2CVf4/P5+PCHP8wf//hHjEYjt99+O1/+8pfjSgH89Kc/5YorrqC5uRmTycTXv/51brnlFgVfycqEQiH27dvHtddem1DqIlqQHA5HzE/dKyFuZ/V4PJSXl6u+/G56ehq32y1FBEq5JMvdEtIRaQQCAZxOJ6Ojo1LKSomaivz7pmN9yPDwMB0dHczPzyvmPRhtISUOWavtOt/Z2UlWVpYU+SWbRQgGg1y8eJG+vr6kBcrj8TA6Osr27duTuqZ4OXXqFEVFRaxfv17Vc1cj5cnf+fl53vGOd7Bnzx6eeeaZS/4+HA7zlre8hdraWg4ePEhvby+33XYb2dnZcS37e9e73iX9/4KCAtXXq4sf3niLiePj47S1tTE5OUl2djbr1q1TRJBERPcHk8mEy+Xi5Zdfpq6uDofDkdIOvpmZGdxuN729vdTU1LBnzx5Fayh5eXls2LDhkj1QVqs1pTdyeW1M3qyhFMXFxZJRbGdnJ36/X5XZoPHxcTo7OwkEAosaAJRA7o4h1uA8Ho8kUKmsg46MjNDe3s78/DxNTU3U1dUpltbOzs5m8+bNtLa2cvHiRbq6uujq6sJkMrF+/fq4fn7pWqv+mm9+ePbZZ7nvvvsuiZj+67/+i7/8y7+UisAA3/nOd/jUpz7F4OBgQh/6zZs3c/vtt/OhD31IiUuPmT/+8Y/s2rUrphvw+Pg4Fy9eZGpqiuzsbGldeaqbFcQOvuHhYSwWi+J2RfKifnV1NXa7XZVUm/zGmoob+czMDE6nk/7+/qRSkfGQTO0qVsT3g+hknmphh0u38NpsNsVHAuSddmoN/QaDQdra2ujr6wOgvr6e1tbWmH6eonhu2rQppdcYzZEjR6TUcSaRdqk8dOgQmzdvXrTW9+abb+bDH/4w586dSyi0LSwsTPkW26WIZch2bGyMtrY2SZBaW1tV7Z4Th1nFDr6uri5F3Lnl68irqqrYtWuXqinDkpKSJfdAJXvDi+6eUzryWwmdTkdFRQXl5eVSpOH1ehW5kctTofX19YruRVoNnU5HTU0N1dXVi/wFk13sCAuvy+l00tfXh9lsTunKkGiys7PZtGkT69ato62tjZ6eHnp6emISqHRGTJnSlCIn7cLU19d3ya558b/FJ494KSgoSIswrTTLFC1I69evT+vgaGlpKVdeeSWDg4N0dHTg8/kSKnLPzc3h8Xjo6uqisrIy7b5v4o1cLN57vd5LhgpjQZw38vv9VFZWqi60cnQ6HdXV1VRVVUkbemM1io0mGAzidrvx+/1pN44V/QVramqkuS+Xy5Xw65J32qXToT0RgYpEImmZ+wqFQhk5b5aQMD3wwAN89atfXfFrLly4QGtra0IXlSzpjJiihWlsbIyLFy8yPT1NTk5O2gVJjnjDq6yspKenRxomjKWDb35+XjIiraioYOfOnRnTvi0+kVdVVdHT00N7ezsejyemdudgMIjP58Pr9VJWVpZxr6uuro6amhrJKFYUqNUWMMrb7UtKSjL+dbndbux2+6oPFPJOO6PRmFGvSy5Q7e3tkkDV1dWxfv36RYIQDodVHxNYbQdUOklImP7+7/+e97///St+jd1uj+l71dbWcuTIkUV/1t/fL/1dImRCKi9akDZs2JDS9eLJoNfrMZvN1NbWSgaQ5eXlNDc3X/I0LXfGFqMutRa/xYv4uurq6vD7/Zw+fXrZAVH5jbuoqCijDVblRrFdXV2SUay4gFF+I5cPVObl5bF169akljSmEvnr6unpoaOjQ4qgoiP56E67TZs2KeZ7qTTZ2dls3LiRlpYW2tvb6e3tlbpJRYHSmh8Wk9AVVVVVUVVVpcgF7Nmzh0ceeYSBgQGqq6sBeOGFFyguLmbDhg0JfU+j0cjIyIgi1xcPer2eQCAgDRbm5OSwcePGpPPmaiFvU3e5XBw+fFjq4NPr9fh8Pnw+35pzxjYYDFitVmkP1LFjx6isrJQGK7u6unC73eTl5aV1oV286PV6GhsbMZlM+Hw+Tp8+TVFRkSS8oqWTTqejtbU1owatV0J8oKivr6e7u5u2tjZJoKqqqhgdHU1Zp10qWU6gamtrCQaDaRGmyyqVFw8+n4+RkRF8Ph/hcJiTJ08CSPM0b3rTm9iwYQPve9/7ePTRR+nr6+Ohhx7irrvuSriduaioSHIIVouRkREmJycJBAJrTpCiyc3NZf369TQ2NtLe3s7+/fvR6XQUFxezbdu2tA7qJkN2djbNzc00NDRI68P1er30eldLh2Uq4gJGs9mMx+Ph+PHj6HQ6yXU+UyP11ZA7tHd3d3Pu3DkEQUAQBOx2+5pZrxGNKFCtra1SDUpMq9XW1qomFJFIBEEQLp+IKR4efvhhfvCDH0j/LXbZ/fGPf+T666/HYDDw29/+lg9/+MPs2bOHwsJCbr/9dj7/+c8nfGZhYWHcW2wTZWRkhLa2NmZmZsjOzsZgMJCdnU1OTs6avBmIhEIhBgYGGB8fp7CwEEEQmJ6eZnJykpKSkjX72gRBYGxsjNHRUXJzc8nJyWFiYoKxsTHKysoy8ukxVqanpxkfH0ev12M0GhkfH2d4eJjS0tKM2YybCMFgkImJCSKRCMXFxUxOTjI4OEhxcfGaiW6XIy8vD51OR15eHoFAgBdffJG6ujo2bNiQ8vdiKBQC1N3UGyuXjSWRnEcffZSf/vSn7N27NyXfX/QCEwUpNzeXlpYWKRXp9/txuVyUlJTQ0tKyZtyjYSHnLC6zE/e4iDUJsYMPErepSRei+0BnZyfBYHCRq4GYflVz5kVJxKHf4eHhRW4R0a3uDocjbZ1qiRBtP9XU1ERBQYFUD/R6vRQWFi56j64FBEGQmjzEe0dZWRnhcJj29na6u7sRBIHa2lo2btyYMoGanp5m37593HLLLRn3oHlZCtPTTz/NE088wdGjRxX9vpFIRGr7jhak6F/s/Pw8brebrq4uVZwWkiUcDtPd3S3VWpbbfBntwdfS0pLxtaZov7bl7HDkq8GVmO1KNfKZneVW08PCDcjlcqk6HJwMkUiErq4uXC4XRqOR5ubmJRtsQqGQJFDittZMTzOLThTBYJCmpqYl2+LD4bA0Y5hKgZqYmODw4cO85S1vybgHzMtSmH784x/z4IMPcu7cOUW+XyQSkQquqwlSNHKnBavVisViyaincbFry+12S8vFYqm1hEIhvF4vXq932Q6+dCPuCBLdIBoaGlbNp8tdCSKRCE1NTXHPQKUa+YxVdXU1DocjpqHfaDslq9WaNqPYpZDv4NLr9TQ3N1NZWbnqz15s8Rcbc8Q19pnE1NQUHR0djIyMSKtrVrsPRAtUTU0NmzZtUkygRkZGOHXqFDfffHNGvb/hMhWm3/72t9xxxx3SorZEiUQi0hPO7Owsubm5rFu3jqqqqrifpMXUX6Z0EomRj9vtllzME0nNzc3N4XK5pAHC5Z7a1WRiYoLOzs6kjFDFtQBOp5OcnBxpD1Q6f2eiIa/X66WkpITm5uaEhpnF1OXY2FhKbKkSQYxW5+bmJMeOeH/W0aMMDocj7aMM8/PzuFwuuru7qa+vx+FwxP0wsJRAbdiwIenP2eDgIBcuXOCmm27ShEkN9u7dy1ve8hZ6enoS+oFHC1JeXh4tLS0JCZIccfaio6ODnJwc1q1bp3rqQbzhulwuyeBViYgg1R58sV6D0qsj5DU3o9FIU1OT6k/j8lmk/Px8aV4pWcSdSVNTU2nbmTQ5OUlHRwdjY2OK1ffE4W+/309ZWRkOh0P1odtIJILP58PtdlNaWkpzc3PSteZwOExnZyddXV1EIhGqq6vZuHFjwgIluogotUNOSS5LYTp16hRXXHEFw8PDcb3JlxKkdevWUVlZqWitQSzeut1u1dJgYsFV3Fwp+pIp/aQ0NjYmpTzVqtOosWxP/jReUVGhykp0+Yp2vV5PU1OT4i3tahjFLkW0p10q9k/J67yVlZXY7faU22XJ05EGg4GWlhbFNy4rJVBdXV10d3crvnVbCS5LYRIt9bu7u2N6I0YiEWkPjShIra2tVFRUpPQDOjc3h9PppLe3F5PJhN1uV/zDKd7cXC6XNP+R6vkqcYuu2MHX3NyckhkhuXGsWuvJ5anL2tpaHA6H4iIoCIL0fgyFQjgcjpSnfuWbj8Ph8Ipr4ZNhuU67VBJtMGy321PyUCF/KGtqakooHRkP4XAYp9OJ3+8nEolQVVXFpk2bYhYor9fL0NBQyte5J8JlKUzj4+OUlpZy/vx5zGbzsl8nClJ7eztzc3OqCVI0k5OTtLe3Mz4+nvDG2mjUWC2wGtEpKKU6+OQ+fZWVlapEL9FMT0/jdDoZGBhQ9IlfdH2fmppKy8ZXeZSm0+kSMsBdilg77VJJqlayzMzM0NHRweDgoNTgpGYaO1GBcjqdTE5OctVVV6l0pbFzWQpTKBQiNzeXQ4cOLbmZMRKJMDQ0JBVb8/PzaW1tpby8PK3twaJIhsNhmpubE15nLa5ICAaDqixjWw15B19FRQVNTU0J3RDE7+Pz+SgpKcmI4rZ874+4yTaRm9Lk5CSdnZ2MjIykZXNtNGJzjMvlIjs7m6amppg65KJJtNMulUQvsUw00pY7tacqeo6HcDiMy+XC5/MRiUSorKxk06ZNy16TaOu0Y8cOla90dS5LYRIEgaKiIn7961+ze/du6c8zVZDkCIJAd3c3TqeTgoICWlpaYrr5pnt9dSwk2sEnb0AoLCykqakp4+ZV5LNS8dTW5PUxk8mEzWZLe1ejnHA4LHkJFhQUxNV4oUSnXSqZnp7G7XbT19dHbW0tdrs9pgFkefRXVFRES0tLWle9RBMOh3G73Xi93hUF6vz58+j1erZu3ZqmK12ey1aYqqqq+Od//mfe+MY3EolEpPx5pgpSNKFQCI/Hg9frpbq6WjIcjUasRzidTmZnZ7Faraqnf+Il1g4+eSowNzc3I1q2V0Jep4lEIivuFZIX5uOZRUoXoVBIejhYbVYoFZ12qWRqagq3201/f/+KzTPiw197ezsALS0taY/+VmI1gTpz5gz5+fls3LgxzVd6KZetMFksFj73uc+xbds2+vr6mJ+fp6CgQKohrRVmZ2fp7Oykv79/0eprsZvK6XQyPT2dtnbfZFiugy8SiUh1DoPBsOTag0xG7IB0Op1kZWUtSoPJ05plZWU0NTVl1NP2asi7E6OvX95pl6pmnlQyOTmJy+VicHAQk8mE1WqVbuKBQID29nYmJydxOBxpT4/HQzgclh5yw+EwFRUVNDc34/F4KC0tZd26dem+xEu4LIUJFlZzzM3NEQ6Hef7551m/fv2a8tOKRv7BqKurIxAIMDk5GbOjQaYiNml0dnYiCALV1dUMDg4iCEJCm0wzieg0WElJCX19feTn58e0jDGTkUd8FRUV5OTk0Nvbq1qnXSqRO2TU1tYSDocZHBxc9GC4FhEF6tChQ3zyk5/kuuuu42Mf+xjXX399ui/tEi47YfrlL3/JvffeS3d3NzqdjjvvvJOvfe1ra/bGLWd0dJSLFy8yOTlJdnY2ra2tGWeXkwhiCuzixYtSd+TGjRvX9IOEiFgzFFu/xT1jaylKWo5IJILb7cbj8UipotbW1jVlFLscoVBIMlQFMJlMNDU1rakIcDn8fj933HEHhw8fpqCggE9/+tPcfffdGbP5FyDlsajH4+GDH/wgNpuN/Px8HA4H//AP/8D8/Pyirzt9+jSvf/3rycvLo6GhgUcffTTus2688Ub+1//6X5SXl3Po0CEef/xxfvWrX7F7927+8Ic/sFY1eHx8nFdeeYUTJ05QVVXFtddei91u5+LFi7zyyitMTEyk+xITZmRkhKNHj3L+/HksFgvXXnst9fX1nDx5klOnTjE1NZXuS0wIUWwPHz6M2+1m3bp1vP71r6ekpIQjR45w9uxZ1VazKI3YUn7w4EH6+/vZsmULV199NVlZWRw8eFB6wFiLiA8SBw4cYHJykp07d7Jr1y7m5uZ46aWX6OjouOTetVaYnZ3lC1/4Ajt27ECn0/Hyyy/z61//mv/8z//EarVy9uzZdF+iRMojpt///vf87Gc/493vfjdNTU2cPXuWO++8k/e97318/etfBxbSVC0tLdx00008+OCDnDlzhg984AM8/vjj/N3f/V3MZ/32t7+lpqaGnTt3Sn82NjbGZz7zGZ555hmuv/56Hn30UZqamhR/nakgEAjgdDoZHR2loaEBi8Wy6Ikt09pV42F8fJzOzk4CgcCSbdaZ6MEXK/JZJLvdfomTgtztO1WuB6litU47se1dvoJjrby21cY1xsfHcTqdjI2NZURLf6wIgsDPf/5z/uEf/gGdTseXv/xl3v3ud0uvTRAE9u7dy+te97qMySylJZX3ta99jaeffhqXywUsrKn4zGc+Q19fn/QmfuCBB3j++ee5ePGiImdevHiR++67jz/96U984AMf4OGHH86o0FXOxMSEtF8nFhfo6elpOjs7GRoawmKxYLVaM7YJQnxtIyMj0mtb6cMtd2XOFMPR5ZCbx8ZyrfF+fToRO+1GR0djcscWb+Lj4+PSTTzTX9vY2Jj02lZqbBgdHcXlchEIBGhsbKSxsTEjBUoQBE6cOMHHP/5xzp07x3333cdnPvOZNfHwmhZheuihh/j973/PsWPHALjtttsIBAI8//zz0tf88Y9/5A1veAMjIyOKFYkFQeC//uu/uP/++xkZGeHBBx/kb//2bzPmJi4vuprNZqxWa1xRgtjlNjs7m3FzI1NTU7hcLgYGBhKa15F38ImvLVO6omZmZnA6nQlHQKOjo3R0dDA9Pb1khJVOku20EztH02kUuxzz8/M4nU56enqSem2Z2IQ0MDDApz/9aX75y1/y9re/na9//euYTKZ0X1bMqC5MnZ2d7Nixg69//evceeedALzpTW/CZrPx3e9+V/q68+fPs3HjRs6fP7+ke0MyhEIhnnjiCb70pS9RV1fHo48+ynXXXZe2m3j0TVvephov4qR9R0cH2dnZtLS0pLWJINpgVaw1JoK8g0+n06XE1DQe5CsNkt0QK9+wq5Y/3kqIbeFerzfpTjtx1k6cI0z3EkYlTZQzbWxjbm6Ob37zmzzxxBOsX7+eJ554gl27dmXMA2qsJCxMDzzwAF/96ldX/JoLFy7Q2toq/Xd3dzfXXXcd119/Pd/73vekP1dbmETEqOnZZ5/lpptu4tFHH8Vms6XkrKWQ1xqSvWlHI7oliLb7LS0tqi7ykxtnKulLBosHbwsKCmhublZ1FUUqZ5FWmoFSA7mrQWFhYczOI7EQvYRRNIpV67WJTRudnZ2KP7Sle9BdEAR+/etf89BDDxEOh3nkkUf4m7/5m4yJTuMlYWEaHBxkeHh4xa+Rh8Y9PT1cf/317N69m2effXbR05JaqbzlOHfuHPfeey8HDx7kgx/8IA899FBK23nlUUQ8ViiJoMSisngQ3aP9fj/l5eU4HI6U/SyV8uCLleibdioFMfqsVNswqelpJ1/BotfrVRmgFjdQz83NpXRR51LWYKlOzZ4+fZqPf/zjnDx5knvuuYeHH354zbfsq5LK6+7u5oYbbmDHjh386Ec/ukTFxeaH/v5+qYj46U9/ml/96leKNT+shiAI/OY3v+HjH/844+PjPPTQQ7z//e9X9IlDKfPIRIgu8CqdbgiFQvh8PrxeL8XFxTQ1NalmsCpfH5KKDr50RjFqOEWInXby2qQaaTYx8nW73eTk5OBwOBT/uU5PT9PR0cHw8LDk/K1GFKGGmfLg4CAPPfQQP//5z/mrv/orvvGNb9DY2KjY908nKRem7u5urr/+eiwWCz/4wQ8WvSlqa2uBhQ6edevW8aY3vYlPfepTnD17lg984AM89thjcbWLK0EoFOIb3/gGX/3qV2loaOBrX/sa11xzTVIfllTZ7SeCuAgxGAzS3Nyc9IBuMiafSiN36Faiyy16R1FTU1PanCjkTgvJ1rNE5J12at60o5G/hwoLC3E4HEm/h4LBIC6Xi66uLurq6nA4HGkZN4heP6PEnqtgMMjjjz/OY489RlNTE48//njS96hMI+XC9Oyzz3LHHXcs+Xfyo0+fPs1dd93F0aNHqays5J577uFTn/pUKi9tRYaHh/nkJz/Jj370I26++Wa++tWvYrFY4voec3NzeDweurq6UrqgLF7ECKCjoyPhPUnytQipetpNFLHLLZkOvkztlJN3ACbqRzc7O4vL5UrpgspEUCLqjkQi+P1+XC4XJSUlNDc3Z4TLhhILO8Wszmc+8xlmZmb4whe+oHhWJ1O47CyJlOb06dPce++9vPzyy3zoQx/iM5/5zKrpt/n5eanOIq7hzoQPRzRyc8fKysqYOq/SUR9IhEQ7+NbKbFEi0aHcsT6TPe3kW25j/fyI0W17ezt6vV5y/s40RINil8uFTqeTBGq19+XZs2f5xCc+wbFjx/jIRz7CP/7jP6Yt66IGmjDFgNjx8slPfpLp6Wk++9nP8t73vveSJxVxs6rf76esrAyHw5GxQ7xy5LMqDQ0N2Gy2SwYGo1MS6W5njhWxjuF0OldsWFirbgzRG2+X2n6cyk67VBLd2bncapDx8XHa29uZnp7OuBm35YhEItIDnsFgwG63L5lWHx4e5uGHH+a5557jLW95C9/85jexWq3puWgV0YQpDoLBII8++ihf+9rXsNvtfO1rX2P37t0MDg5KNi2lpaUZsVk1ESYmJmhvb2diYkJKX+l0uoyaQUkUMVoQn8LFDj75zS/VHZKpYrkZKFgYtOzo6MiY7bGJED0LJ+5LmpmZobOzk4GBAcnxJBOj25UQU+Jut5usrCyCwSDXXHMNkUiEJ598km984xtYLBYee+yxtM5aqo0mTAkwMDDAJz7xCX7yk59gs9nw+/28+93v5gtf+IKq8zSpQLzJiW7YWVlZzM/Pp31wUCnEDr6enh6KioqYnJyUhCoT6n/JIJ/TAdDr9ZJQrYUoYjXEQfT+/n6MRiNTU1OKNYKkm0gkgtfr5U1vehOw8LsMh8N87nOf484771zzn7t4Wdvv1DQhNgzk5eXR19eHIAiUl5evGZPRldDpdOTk5JCbm0swGGRmZobCwkLKy8sviw9HVlYWhYWFGAwGyd27uLh4zd/YYOF3V1RUhNFoZH5+XlohUlhYuOZFCRY+d2VlZWRlZTE7OwtAbm7umouSlkKv1zM7O0tTUxODg4OMj49jNpuxWCyXxe8uXrSIKQF27txJTk4OX/jCF7j++uv5xS9+wQMPPEAwGOQf//Efeec737kmb+Jyrz7ReFOn00kpsJqaGpqamtaECWQ00bNIzc3NVFRUSDWaTPTgi4elOu30ej0+nw+Px7Mmt+XKEVeaRyIRWlpaqKqqIhAIrOhQv1YYHR3lH//xH/nhD3/IzTffzGOPPUZtbS1PP/00X/3qV/nc5z7HRz7ykXRfpqpowpQAvb29l3TSzM3N8ZWvfIVvfvObtLS08PWvf50rr7xyTeSEp6encTqdKxqsrtV8vnwWKRKJLLkVV2zsEGsx6fbgi4foTjuHw3FJt5Z8BmqlJoJMZGJigo6ODsbHx3E4HEu27Y+MjNDZ2cn09LTktLAWHgxDoRBPP/00jz76KPX19Tz++OO84Q1vuGSNiCAIa/aBIlE0YVKYvr4+Pv7xj/OLX/yCt771rXz5y1+WCtGZhvwpO9bCf3QHlMlkytgbuHjDmpmZialpI7qDL5O71xKxR5I3ESTi8K4mcjcPs9mM3W5fcbWE3Apofn4eu92esdGvIAi88MILPPDAA4yOjvLZz36WD3/4w2tCTNVCE6YUIAgCx44d49577+X06dPcc889fOITn8iYFJj8CXq5p+yVkEcYBoOBlpYWKioqUnjF8SF/yk4kxSOPQsT5rkyZGVEiupPPQGXawrtwOIzX68Xj8VBRUUFzc3Nc0V20UexSEXI66ejo4BOf+AT79+/nb//2b3nkkUfWxEiJ2mjClEIEQeC5557jwQcfBODzn/8873jHO9K+ysDn8ylSc4iesm9paUlrZ5s8JRnLgsXVyDSHBCUcLeREz0ClMwUm1gA7OzvJzc2lpaUlKdNacZD1/2/vvuOaPNf/gX8CgmwUEYGyIgS1yhBQZDioVLTW2h5ra/Ur1Hq0zjIUxAGiRxTBgauOHhWOraN6jta2SuuxbraAg5kEEGXJMIwoAZLn94e/5ARFZSV5Avf79eL1qkma5w7juZ77vq/nurhcLlRVVRV+I3h9fT0iIiIQFxeHDz74ALGxsbC1tVXIWJQB/ea5/19xcTEWLlwoaQVhbW2NjRs3orm5uc3r7t+/j/Hjx0NDQwPm5uaIjo5W0Ihfx2AwMG/ePBQUFMDX1xcrV67E5MmTkZGRAXleD7S2tqKoqAi3b99GXV0dnJycMHr06G6vW6uoqMDS0hIeHh7Q1tZGSkoKcnJyIBAIemjkHSMQCJCbm4ukpCSoqKjA3d0dtra23Q4iGhoaeP/99+Hq6oqmpibcvn0bhYWFEAqFPTTyjmlsbERWVhYyMzNhaGgIT0/PHimRNGDAALi4uGDUqFEoKytDYmIiSktLIRKJemjkHVNbW4uUlBRwuVywWCyMHTu225XUVVRUYGpqCg8PD5ibmyMvLw8pKSmorq6W69+eUCjEwYMHYWdnh1u3buHChQv4/ffflTYoWVlZgcFgtPmKiopq85qeOCfTdsaUkJCAM2fO4KuvvoKNjQ0ePnyIRYsWYf78+dixYweAl1chtra28Pb2xtq1a/HgwQN88803iI2NlXvx144oLS3FqlWrcP78ecyaNQtbt26FkZGRzI4nLo5ZXFwMTU1NmRdY5fP54HA4qKmp6VD77e6SngGKS9fIcsYmbp0gryrc8pyxie+B4nK5cksA4fP5YLPZqK2tlfnvi7g/WXFxsdzaiFy7dg1r1qxBZWUlNmzYgBUrVihFwtDbWFlZYeHChZImrwCgq6srWeruqXMybQNTe2JiYnDw4EEUFhYC+F+7jIqKCskfbGhoKC5cuCC3dhmdRVEUkpOT4e/vj7y8PPj7+yMoKKhHN6GlC6yqqanJvdncs2fPkJ+fj5aWFplU5JY+yejo6IDFYsktSeHVPR5ZVFN4U5UKeZBuwiirixl59wiTJt1GRFZVWrhcLkJCQnDt2jUsWLAAW7dulXlPOXmxsrJCQEAAAgIC2n2+p87JShWYNmzYgISEBKSnpwNQfIPB7hAKhfjxxx+xfv16qKmp4R//+Ac+++yzbp3gpK96GQwGrK2tu93WortjYbPZPbJnAPyvvhiXy4W6ujpsbGwwaNAghXw+WdSf62hdP3kQJyE8evQI+vr6sLGx6fYmvUgkkrQ0HzBgAFgslsL2JKULLRsaGvbIbLuhoQGbN2/GsWPHMGHCBMTGxsqs+7aiWFlZoampCS0tLbCwsMDcuXMRGBgomQn21DlZaeaVHA4H+/btkyzjAS9Ts19thT5kyBDJc3QOTKqqqvDz88MXX3yBTZs2YcmSJTh06BB27NgBe3v7Tr3Xq32DeqLnS3cxGAyYmJjAyMgIJSUlyMzM7PLV/6uZVra2tgoLuGIqKiqwsLCAqakpiouLkZ6e3uWK3a9WQn///fcVfh+VuLComZkZiouLkZaW1u3PJ87itLe3V3gWp7q6OmxtbWFpaYmioiKkpKR0uXmnUCjEsWPHJDOjc+fO4aOPPqJNJmBP+u677+Dk5AQDAwMkJiZi7dq1KC8vx65duwD03DlZ7meu0NDQ1zbPXv16dcpXWlqKqVOnYvbs2W3WNnsDTU1NREVFIScnB8bGxpg4cSK+/fZbVFVVvfP/pSgKNTU1SE1NRW5uLszMzODh4UGrIquqqqpgMpnw8PCAmpoakpOTJct8HVFbW4vU1FTk5eXB3Nwc7u7utEr/FXe09fDwQL9+/ZCUlIS8vLzXknTe5NmzZ0hLS0NeXh4sLS0xbtw4WrUREZ/APTw8oKqqiqSkJOTm5nY4wYXH40k+H5PJxLhx4xQelKT1798fw4cPh7u7OxgMBpKSkpCTkyMpefQ2FEXh5s2b8PDwwObNm7F69Wo8fPgQ06dPp83PryM6c04OCgrCpEmTYG9vjyVLlmDnzp3Yt29fjyc8yX0pr6qqCjU1NW99jfQmb1lZGSZNmoRx48YhLi6uzQlXmZfy2kNRFG7fvo2AgABwuVwEBgYiICCg3XtMeDweOBwOGhsblarAamNjIwoKClBXV4ehQ4e226YBgKTcTF1dHaysrGSeSNFTOtoVVvpeIkV2j+0s6bJV4j5Q7f1+vnjxAmw2G1VVVZLPpwwb/3w+H1wuF1VVVW9tf/Lo0SOEhITgzz//hK+vL7Zv366wzs3d1dlzsrTs7GyMGjUKeXl5GDZsWI+dk2m9x1RaWgovLy84Ozvjxx9/fO0PV7zRVllZKfnjWLduHf7zn//QNvmhI4RCIY4fP47w8HBoamoiMjISM2bMAIPBQFFREZ49ewYejye5eZQuN0d2Rk1NDQoKCiAUCsFisSSzBOkTw5t6QymD2tpasNns1zL4pCsamJqaYujQobStvvA2dXV1YLPZr10YSTf5MzY2hrW1NW1uLO8M6YaRJiYmMDQ0xODBg9HY2IjIyEj88MMPcHNzQ2xsLOzs7BQ9XIX56aef4Ovri+rqagwcOLDHzsm0DUylpaWYNGkSLC0tER8f3yYoGRsbA3j5xzFs2DBMmTIFa9aswcOHD/HNN99g9+7dtEwX7yw+n4+NGzfi+++/x4gRI9CvXz9wOBwkJCSAxWIpRSO7t6EoSrLZr6GhAQ0NDVRXV7fpuaPMpPdWxJW/q6ur5Z5pJysURUkCsEAgwMCBA1FbWwtdXV2wWKxeUdGAx+MhPj4ekZGR8PDwQFZWFvT09BATE4OZM2cq1ZJddyUlJSElJQVeXl7Q1dVFUlISAgMDMW3aNMTHxwPouXMybefWV65cAYfDAYfDgZmZWZvnxLFUX18ff/75J5YvXw5nZ2cYGhoiPDy8VwQlANDW1sayZctQWFiIX375BQDwt7/9DcbGxkoflICXCRJDhgwBn8/H48ePUV9fD0NDQzCZTKUPSsDLzzd48GAIBAJJzT5tbW1YWVkpfVACXn4+AwMDDB06FHl5eXj69CnU1NTw3nvv9Zqio/r6+nBxcYGRkRGuXr0KDQ0NrFmzBlOnTu1TQQl4uR93+vRpREREQCAQgMlkIjAwEEFBQZLX9NQ5mbYzJgI4efIkvvnmG8yZMwfh4eF49OgRAgMDUVJSglWrVmHFihVKucwF/O9epKKiIujp6cHGxgb9+/cHh8NBZWUlLCwswGQylWJfoj3SmXYAwGKxMHDgQEkKdlcz3OhEuuOxuGuuuF24hoYGWCyW0u67AMDjx4+xZs0aXL58GXPnzsX27duRkpKCDRs24NmzZ7h3757S7WMrCxKYaKy8vBw8Hq/NvRBCoRBHjhzBpk2boKuri23btmHatGlKc/UmffOvurq6pC+StPr6ehQUFKCxsVFSwZwuWYYd8a6adnSrwddZTU1N4HK5qKiogIWFBaysrNpcIAmFQkkfKD09PaVb1nv+/DmioqJw8OBBjBkzBrGxsXB0dJQ8LxKJcPPmTUyaNElhY+ztSGBSUg0NDQgLC8ORI0fg5uaG6OhoWt/M9+oMwsbG5q1p0eJ7s8T7M7KosNDTpEvsiDPW3jbjk87gk0cJp+6SrppgaGgIFov11jYp0okQXaliL29CoRAnT57Epk2boKmpie3bt2PWrFm0/p3rrUhgUnJcLhcBAQGStNWIiAjaLS/U1NSAw+F0qcacdIUFXV1d2Nra0m7/oruZdtIJBOLvD51OhhRFoaysDBwOB5qamrC1te1URQrpGSIdE1soikJqaiqCg4PBZrOxevVqhIaGKu0yeW9AAlMvQFEUrl69isDAQJSVlSEkJARLly5V+P5MXV2dpPV1d+9FamlpQVFRER4/fkybNGTpGUR3M+0oikJlZSU4HI7MavB1xZvS+rtC+laA9pYAFaGsrAyhoaH49ddf8cUXX2DHjh2SSgWE4ijPwr2MREZGwt3dHVpaWm+8CiwpKcH06dOhpaUFIyMjBAcHo7W1Vb4DfQsGgwFvb29kZWUhIiICO3fuxNixY/Hnn3/KtcS/GJ/Px/3795Geng49PT14enqCyWR2a5lKTU0Ntra2cHNzg1AoRGJiIrhcrtxbUAD/60N1584d1NbWwsnJCQ4ODt1apmIwGDA2Noa7uzvMzMyQnZ2Nu3fvoq6urgdH3nGNjY3IzMzE/fv3YWJiAnd3926XgdLW1oa9vT1cXFxQX1+P27dvo6ioSCE/wxcvXmDz5s1wdHRERUUFEhMTceLECaUPSgKBAI6OjmAwGMjKymrzHJ1bBL2qz8+YNm7ciAEDBuDJkyc4evQoeDxem+eFQiEcHR1hbGyMmJgYlJeXw9fXF4sWLcLWrVsVM+h3qKurw/r163H06FGMHz8eMTExYLFYMj+uPJdseDyepAWFjY0NTExMZD67aC/TTlY17cQtPeSdwdfc3Awul4uysjKZJ2bU1NSAzWbLtRW6UCjEzz//jIiICKiqqmLbtm2YM2eOwmemPcXf3x9sNhuXL19GZmamJGlD2VoE9fnAJBYXF4eAgIDXAtPly5fx8ccfo6ysTHI1dejQIaxZswZVVVW0zqbKz89HQEAArl27hq+//hoREREyyY5S1Ca3ePmLzWZLZlSySk8WB8Ke6h7bUdIZcLIMFNKZdAMHDgSLxZLbz7AzSTHdOU56ejpCQkKQm5uLwMBArFu3TimrbrzJ5cuXERQUhH//+98YOXJkm8CkbC2C+vxS3rskJSXBzs6uzRTfx8cH9fX1yM7OVuDI3m3YsGG4dOkSzp8/j5s3b8LOzg4HDx7ssaUToVAo6YxbX18PFxcX2Nvbyy3zSnr5y9jYGPfu3UNmZib4fH6PHYPP5yMrKwsZGRkYNGgQPDw8eqR7bEdpaGhg5MiRcHV1xYsXL3Dnzp0eXf4StzRPTExEZWUlHBwc4OjoKNef4ZAhQ+Dm5gZLS0vk5+cjJSXlnbXbOqOyshILFy7ElClTYG1tjby8PGzatKlXBaXKykosWrQIJ06caHdmnZSUhAkTJrS5qPHx8UF+fj6ePXsmz6F2iHLevShHFRUVr607S5dxpzsGg4Fp06bhww8/xL59+xAZGYljx45h+/bt8PLy6tKVqfheJHEpIUW3MVBVVYWVlRVMTU3B5XKRnJzc7dmFQCBAYWEhysrKJC26FXki09HRwejRoyUZfI8fP+52Bh+Px0N+fj4EAoHclkPfREVFBWZmZjAxMcHjx49x//59yY3XXe1z1dTUJKl+/f777+PmzZsYO3Zsr1m2E6MoCl9//TWWLFkCFxcXFBcXv/YaZWsR1CtnTF1prdHb9evXD4GBgWCz2Rg/fjw+//xzfP755+ByuR1+D3Hzv8TERDx69AjDhw/H2LFjadPGQF1dHSNGjGgzuyguLu7U7KK1tRVcLhd37txBc3Mzxo0bhxEjRtDm6trAwABjx46Fra0tioqKkJSUhKqqqk4luTx//hz37t1DRkYGBg8eDA8PD9qkqIsvMjw9PaGnp4f09HTcu3evU7NgiqJw7tw5jB49Gv/6179w4MABJCUlwdXVlRafsaM6eh7bt28fGhoasHbtWkUPucf0yj2mrpRxf9MeU3h4OC5evNgmw6WoqAhDhw5FRkYGRo8e3ZNDl5vc3Fz4+/vj1q1bWLhwIcLCwt54f5B0sU55blR3V21tLQoKCtDS0gIWi/XWrLJXW4p39l4dRZC+x6sjLeZbWlpQWFiIJ0+ewMTEBNbW1rQJuG/S2YSazMxMBAcH4/79+/D390dYWJjCbyvoqo6ex7744gv8+uuvbX63hUIhVFVVMW/ePMTHxytdi6BeGZi64l3JD+Xl5TAyMgIAHDlyBMHBwXj69Cnt/7DfhqIo/P7771i1ahV4PB7Wr1+PBQsWtEnrFrc3aGhoAJPJVJq+T2LvujlUusIEINtMO1l5VwafMtyk/C7vaodSVVWFdevW4dy5c5g5cyZ27twJc3NzBY5YfkpKSlBfXy/5d1lZGXx8fHDu3Dm4urrCzMxM6VoE9fnAVFJSgtraWly8eBExMTG4desWgJfZQTo6OpJ0cVNTU0RHR6OiogLz58/H3//+d9qmi3dWa2srdu3ahaioKJiZmSE6Ohq6uro4ePAgvvjiC1haWtLiZsjuEAqFKC4ulpTTsbGxQXNzsyTTbujQoUpXk+9V0hl8ZmZmsLKyQl1dHQoKCqCiogJbW1sMGjRIqYLuq8QNJDMzM8HlchEUFISjR48iNjYWtra2iI2NlXSj7auKi4vBZDLbZOUpW4ugPh+Yvv76a0kvEWnXrl2TFGl89OgRli5diuvXr0NbWxt+fn6IiopSeGWFnlZTU4MVK1bg559/BgBMnToVR44cod00vzuampqQn5+Pp0+fgsFgwNLSUqmrmLensbEROTk5qKurg6qqKmxsbOSaSSgP165dw5IlS1BeXg4dHR3ExMTgm2++UarZvKy0F5iAlzfYLl++HGlpaTA0NMTKlSuxZs0axQ30Lfp8YCJeqq2txbZt23DgwAFMnDgRz549w/3797F48WKsW7cOOjo6ih5it0ln2hkaGqK5uRl8Ph9Dhw7tNSfupqYmSesQIyMj8Pl8NDc307IGX1c9ePAAwcHBSE9Px4cffoi8vDyIRCJs2bIFs2fP7hU/x76u91wmEt1y48YNPHjwALdv34aTkxMoisKFCxcQEhKCM2fOYMOGDfD19VXKK9JXa9qNGzcO2traoCgK1dXVkvRrZdxfEmttbZUsVRoZGcHd3R2ampptavCVlJTAxsaGFjX4uqK6uhphYWE4ffo0pk+fjtzcXFhaWqK1tRVxcXHYuXMnZsyYodQ9roiXyIyJAPAyCaC9k1VLSwtiYmIQExMDS0tLxMTEKM0afkcz7cSv43K50NHRga2trdL0D5K+p0xLSwu2trbtZuZ1NoOPTlpaWrBnzx7s3r0bVlZW2LNnD8aPH//a7+CbfocJ5UMCE9EhVVVVCA4OxqlTpzBt2jRs376dtllPr2badbTMjXRppSFDhsDGxobWqcbV1dUoKCiASCSCra1th2Z7r5aPonMXXYqicOnSJaxduxbPnz/Hpk2byD5SH0ECE9EpmZmZCAgIQHp6OpYuXYq1a9e+tVmcvEnXtOtqpt2LFy/A4XDw9OlTSUYinZIjGhoawGazUVdXh6FDh8Lc3LzTn/HVDD4mk0mruo85OTkIDg5GSkoKli5dik2bNvWKfU6iY8guoRwdOHAAVlZW0NDQgKurK1JTUxU9pE4bPXo0rl+/jri4OPznP/+Bvb09Tpw4oZDWBdL4fL6kmoG4pl1XTtgAoKmpCTs7O7i4uODZs2e4c+cOnjx5opAWItIEAgFycnKQmpoKbW1teHp6wtLSskufUVyDb+zYsXj+/HmP1+DrqtraWqxYsQIeHh7Q09PDw4cPsXPnTqUNSp988gksLCygoaEBExMTzJ8/H2VlZW1eo0ztKOSFzJjk5MyZM/D19cWhQ4fg6uqK2NhYnD17Fvn5+ZIbd5VNc3MzoqKisHPnTrBYLMTExMi9FtmrNe062z32XcTVr9lsNlRVVSX3AsmTUCjEo0ePUFxcjEGDBoHFYvX48pu4SoaiMvhaW1uxf/9+7NixA2ZmZoiNje1yLUc62b17N9zc3GBiYoLS0lKsXr0aAJCYmAhA+dpRyAsJTHLi6uqKMWPGYP/+/QBebkabm5tj5cqVCA0NVfDouqeyshKrV6/Gzz//jBkzZiAqKgqmpqYyPaZ0pp2BgYHM2zSImwMWFhZCX18ftra2Mr+KF1f+5nA46N+/P2xtbWV6T5l0Bp+qqipYLJbMb8ilKAp//PEH1q5dCx6Ph4iICCxevLjX7iNdvHgRn376KQQCAdTU1JSuHYW8kKU8OWhubsbdu3fh7e0teUxFRQXe3t5ISkpS4Mh6xpAhQ3DixAkkJiaioqICjo6O2Lx5M5qamnr8WOLssjt37qCmpgZOTk5yadOgoqICS0tLeHh4QFtbGykpKcjJyYFAIJDJ8Wpra5GSkgIulwsWi4WxY8fK/EZn6TYipqamePjwITIyMtqUu+lJ+fn5mDlzJubNm4epU6eCzWZj6dKlvTYo1dbW4qeffoK7u7ukioqytaOQFxKY5KC6uhpCobDd9hnK0Dqjo5ydnXHr1i388MMPOH36NBwcHHDq1Kke2bcQL6klJSVJKpuPGTNG7oVW1dXVMWzYMIwbNw7Nzc09vjcj7v+UlZWFIUOGwN3dXe7tKKSDsJ6eHtLS0vDgwQO8ePGiR96/rq4OAQEBcHNzQ//+/fHgwQPs2bNHaVL0O2vNmjXQ1tbGoEGDUFJSgl9++UXynLK31ZEVEpiIHsVgMPDVV18hPz8fCxYsQGBgILy9vZGWltbl5AEej4f09HTk5ubCwsICbm5ub60ULg/a2tpwdHSEo6MjKisrkZiYiPLy8i5/xubmZuTl5SE5ORn9+/eHp6cnmEymQmcPampqYLFY8PDwgIqKChITE5Gfn4/m5uYuvZ94H8nOzg6JiYm4ePEifvvtN9jY2PTwyGWrs211goODkZmZiT///BOqqqrw9fVVeCIN3ZE9Jjlobm6GlpYWzp07h08//VTyuJ+fH3g8XpsrqN6mrKwMq1evxr///W989tln2LZt22tXiG/C5/PB4XBQU1MDS0tLWFpa0iptW0zcp4rNZnd6L0gkEqGkpARFRUUYMGAAWCwWbTPQpNPUraysYGFh0aHASVEUrl69itDQUFRVVSEsLAzLly9X2iW7rrTVEXvy5AnMzc2RmJgINzc3pWtHIS/0+yvvhdTV1eHs7IyrV69KApNIJMLVq1exYsUKxQ5OxkxNTfHTTz/B398f/v7+cHBwwMqVK7F69eo3Zs9JZ9qZmJgovHvsuzAYDJiYmMDIyAglJSXIzMzEoEGDYGNj88a9r1ez/RTdBbgjdHV14eTkJMnge/z48Ts733I4HISEhOD69etYuHAhIiMjad/n6l0GDx6MwYMHd+n/FYlEACDZm3Rzc8P69evR0tIi2Xe6cuUKhg0b1meDEkBmTHJz5swZ+Pn54fDhwxg7dixiY2Px888/Iy8vr8MzCGVHURROnDiB9evXQ1VVFZs3b8asWbMkJ7VXM+3ErUeUjUAgAJfLRXl5OczMzDB06NA2LUOkbwK2sbFRyuKq4lkih8NBv379Xsvgq6+vx+bNm3H8+HFMmDABe/bswfDhwxU8avlKSUlBWloaPD09MXDgQHC5XISFhaGyshLZ2dno37+/0rWjkBcSmORo//79iImJkWSu7d27F66urooeltw1NTVh8+bN2Lt3L+zt7bF161bcvHkT+fn5WLRoEVgsVq+4WmxsbERBQYGkQoOhoaGk2Z2VlRVtlyY7Q5xGn52djQMHDiAkJAR5eXnYtm0bDAwMsGvXLkybNk3pAm9PePDgAfz9/SWt4U1MTDB16lRs2LAB7733nuR1ytSOQl5IYCIUpqSkBF999RUSExOhra2NiIgILF26tNedxCorK5Gbm4uWlhYMHDgQI0eOpFUZp55QU1ODJUuW4PLly1BTU8Pq1auxadMmpQ+8hGKQrDxCIZKTkzFv3jyw2WwEBQVh2LBh+Mc//oHo6GiZ3Rskb+LZRG5uLnR0dMBkMsHn8/Hw4UPU1dUpeng9pqioCEuWLMF///tfzJkzB7NmzcKuXbsQEhLyziQBgmgPuZwhFOLixYvw8vLCpUuXoKurC6FQiPj4eISFheHEiRPYsmULZs6cqZSzJ+k+TxRFYeTIkZIeSFZWViguLkZ6ejqMjIxgY2OjtLOnhoYGREZG4p///Cfc3d2RkZGBkSNHAni5PBUaGirpgUUQnUGW8ghaef78OSIiInDgwAE4OzsjJiYGdnZ2ih5WhzU0NKCgoAANDQ2wtrZ+Y3Vz6U6zFhYWStXeXSgUIi4uDlu2bIG+vj527NiBGTNmKOVFBEFPJDARtPTo0SMEBgbi999/x5w5c7BlyxZaX3lLt5EwNzcHk8lsk4n3JvX19SgoKEBjY+NbAxkdUBSFO3fuIDg4GI8fP0ZoaCiCgoKUJqASyoMEJoK2KIrCrVu34O/vj+LiYgQFBeG7777r0AlfXoRCoaSluaGhIVgsVqeX5qQbGzIYDLBYLNq1Py8pKcGaNWuQkJCA//u//0N0dDStLxQI5UbPSzOiw27evIkZM2ZI7oWRvoMceHnSCw8Ph4mJCTQ1NeHt7S3p7Ep3DAYDEyZMQHp6OrZv347vv/8ezs7O+P333xVe0oWiKJSWluL27duSYrL29vZd2i9iMBgwMjKCm5sbzMzMkJ2djYyMDDQ0NMhg5J3z/PlzrF+/Hk5OTqivr0dqaiqOHj2qtEGpuLgYCxcuBJPJhKamJqytrbFx48bXyiyRHkmKRQKTkuPz+XBwcMCBAwfafT46Ohp79+7FoUOHkJKSAm1tbfj4+Mik8resqKqqYvHixWCz2fjkk0/g5+eHjz/+GDk5OQoZT01NDZKTk1FUVIRhw4b1WDFZFRUVWFhYwMPDA7q6ukhNTUV2drZCshTFySj29vb49ddf8eOPP+Kvv/6Cg4OD3MfSk/Ly8iASiXD48GFkZ2dj9+7dOHToENatWyd5TX19PaZMmQJLS0vcvXsXMTExiIiIwJEjRxQ48r6FLOX1IgwGA+fPn5eUPaIoCqampli1apWkQVldXR2GDBmCuLg4zJkzR4Gj7Toul4ugoCAkJCRg3rx52Lx5MwwMDGR+3MbGRrDZbPB4PDCZTFhYWMh0P+j58+fgcDiorq6WtHiXdX05iqKQnJyM4OBgFBYWIjg4GCEhIbRaPu1pMTExOHjwIAoLCwGA9EiiATJj6sWKiopQUVHRpg+Uvr4+XF1dlboPlLW1NS5cuIBLly4hPT0d9vb22LNnD1pbW2VyvObmZuTm5iIlJQWamprw8PCAlZWVzJMUtLS0YG9vDycnJ9TU1ODOnTsoKyuT2TJmaWkp5s+fj48++gh2dnYoKCjA+vXre3VQAl5erElf2JAeSYpHAlMvJu7n0hv7QDEYDEyePBmZmZnYsmULYmNjMWbMGCQkJPTYiVsoFKKoqAh37tyBQCDAuHHjMHz48HarRsvSgAEDMGbMGNja2oLL5SIlJQW1tbU99v4vXrzAxo0bMXr0aFRVVSEpKQnx8fEwMjLqsWPQFYfDwb59+/Dtt99KHiM9khSPBCZCqamqqmLZsmVgs9nw8fHBvHnzMHPmTOTn53f5PcUtzRMTE1FZWQkHBwe5dMl9G+nussbGxrh37x4yMzPB5/O7/J5CoRAnT56Eg4MDzp07h6NHj+LmzZtwcnLqwZHLR2d7JAEvZ4hTp07F7NmzsWjRIgWNnGgPCUy9mLGxMYCXtdqkVVZWSp7rLfT09LB37148ePAA/fv3h5ubGwICAjpd+ofH4yE1NRVsNhvW1tZwdXWVy/5VR6mqqsLKygoeHh7Q0NBAcnIy8vLyOtW8j6IopKWl4YMPPsCqVauwaNEi5OXl4csvv6RVinpnrFq1Crm5uW/9Gjp0qOT1ZWVl8PLygru7+2tJDcbGxu3+zYifI2SP3BnXizGZTBgbG+Pq1atwdHQE8DLjKCUlBUuXLlXs4GTExsYGv/32G65cuYKgoCDY2dlhzZo1+Pbbb996I+jz58/BZrNRU1MjqfxN50Z26urqGDFiBMzNzcFms3Hnzh0wmUyYm5u/ddzl5eVYu3YtfvnlF8yePRsXL16EiYmJHEcuG53pkVRaWgovLy84Ozvj+PHjr+0Vkh5Jikey8pRcY2MjOBwOAGD06NHYtWsXvLy8YGBgAAsLC2zfvh1RUVGIj48Hk8lEWFgY7t+/j5ycHGhoaCh49LIlFAqxf/9+bNmyBYMHD0ZUVBQmT57cZlbQ0tKCwsJCPHnyBCYmJrC2tqZ1U8I3qampQUFBAVpbW8FisV5rPd/U1ISYmBjs27dPkizi4uKitDOkriotLcWkSZNgaWmJ+Pj4NkFcPBsiPZIUjwQmJXf9+nV4eXm99rifnx/i4uJAURQ2btyII0eOgMfjwdPTE99//z1sbW0VMFrFePbsGdavX49jx45h0qRJiI6OhpmZGQ4dOgQ7OzsMGDAAtra20NXVVfRQu4WiKJSVlYHD4eDJkycYMmQIJk+ejLNnzyI8PBwMBgNbt27F3Llz+1xAEouLi8OCBQvafU76VEh6JCkWCUxEn5GXl4fvvvsO169fh4aGBgYMGIDTp0/DwcGhV52ohUIh9uzZgy1btkBLSwsCgQBBQUFYv359r58lE70DSX4g+ozGxkYIBAJoa2tDS0sLL168QEpKCkQikaKH1qNqamqQk5MDkUgEfX19CIVCNDU1KVW1D6JvI4GJ6BOSk5MxYcIEeHh44NGjR3jy5AlCQ0OxZcsWuLm54fr16wqvv9ddAoEA27Ztg729PTgcDm7cuIHCwkIkJycjMzMTnp6eSv8Zib6BLOURfYJ4/+W9995r83htbS1CQ0MRHx8Pb29vREdHg8lkKmiUXUNRFM6fP4+wsDC0trYiMjIS8+bNa7Ox/6bPTxB0RGZMRJ/AYDDaPSkbGBjgyJEjyMjIgEAgwJgxYxAaGorGxkYFjLLz7t+/Dx8fHyxZsgRz5sxBQUEBfH19X0sZf9PnJwg6IoGJIACMHDkSV65cwenTp5GQkAB7e3scO3YMQqFQ0UNrV1VVFb799ltMnDgRxsbGyMnJwbZt25S2TTtBSCOBieiwbdu2YcyYMdDV1YWRkRE+/fTT10r/NDU1Yfny5Rg0aBB0dHQwa9as1+6ipysGg4FPPvkE2dnZCAgIQHh4ODw9PXH79m3a7M20tLQgJiYGDg4OyMnJwX//+1+cPXsWFhYWih5al0VGRsLd3R1aWlpvbB9SUlKC6dOnQ0tLC0ZGRggODpZZ0V5C8UhgIjrsxo0bWL58OZKTk3HlyhW0tLRgypQpbeq1BQYG4tdff8XZs2dx48YNlJWV4W9/+5sCR915ampqCA0NRUFBAZydnTFjxgzMnTsXJSUlChsTRVG4ePEinJyccPjwYezcuROpqakYP3680qe6Nzc3Y/bs2W+sRiIUCjF9+nQ0NzcjMTER8fHxiIuLQ3h4uJxHSsgNRRBd9PTpUwoAdePGDYqiKIrH41FqamrU2bNnJa/Jzc2lAFBJSUmKGma3ZWVlUZMmTaI0NTWpwMBAqqqqiuLz+XL7Sk1NpSZMmEBpampSq1evphobGxX9LZGJ48ePU/r6+q89funSJUpFRYWqqKiQPHbw4EFKT0+PEggEchwhIS9kxkR0mbhAqrjI6d27d9HS0tKm/9Pw4cNhYWGh1P2fHBwc8Ndff+HEiRP45ZdfYG9vj3/9618y33+qqanB8uXL4enpiYEDByInJwcxMTEKrXKuCElJSbCzs2vTisLHxwf19fXIzs5W4MgIWSGBiegSkUiEgIAAeHh4YNSoUQAg6fj56j5Bb+n/NGvWLOTl5WHZsmUIDQ3FxIkTkZSU1OP7Ty0tLdi9ezccHByQmZmJhIQEnD9/HlZWVj16HGVB+iP1PSQwEV2yfPlyPHz4EKdPn1b0UORKTU0NGzZsQEFBAezs7PDRRx9h/vz5KC0t7fZ7UxSFS5cuwcXFBfv27cPWrVtx9+5deHl5Kd0+Ulf6IxGEGAlMRKetWLECv/32G65duwYzMzPJ48bGxmhubgaPx2vz+t7Y/8nIyAjx8fFISkpCVVUVRo8ejYiICLx48aJL75ebm4tPPvkEvr6++Pjjj8Fms7FkyRJat954m872R3ob0h+pD1L0JhehPEQiEbV8+XLK1NSUKigoeO15cfLDuXPnJI/l5eUpffLDu4hEIurUqVOUlZUV9d5771E//PADVV9f36HEhidPnlB///vfKXV1derjjz+mOByOoj+Owrwr+aGyslLy2OHDhyk9PT2qqalJjiMk5IUEJqLDli5dSunr61PXr1+nysvLJV/Pnz+XvGbJkiWUhYUF9ddff1Hp6emUm5sb5ebmpsBRy09TUxO1ceNGSk9Pj3JxcaGuX79ONTY2thuQ6urqqKioKMrAwIAaNWoUdeXKFUokEin6IyjEo0ePqMzMTGrTpk2Ujo4OlZmZSWVmZlINDQ0URVFUa2srNWrUKGrKlClUVlYWlZCQQA0ePJhau3atgkdOyAoJTESHAWj36/jx45LXvHjxglq2bBk1cOBASktLi/rss8+o8vJyxQ1aAcrKyqi5c+dS6urq1OzZsykOhyMJSI2NjdT58+epYcOGUUZGRtS+ffuo1tZWRQ9Zofz8/Nr9vbp27ZrkNcXFxdS0adMoTU1NytDQkFq1ahXV0tKiuEETMkWKuBKEDFAUhfT0dPj7++Pu3buYMGECli1bhmXLlqG2thaLFy9GZGQk9PT0FD1UgqAdEpgIQoYoisKqVasQGxsLiqJgYGCA8+fPY8KECYoeGkHQFsnKIwgZoSgKJ0+exNmzZ+Hk5ARvb29QFIXt27e/VmOQIIj/ITMmgpCRiooKTJw4EWFhYZg7dy5UVFRQU1ODiIgIVFdX49SpU4oeIkHQEglMBCFDIpEIKiqvL0y86XGCIEhgIgiCIGiGXLIRBEEQtEICE0FrBw8ehL29PfT09KCnpwc3NzdcvnxZ8rwyNyakswMHDsDKygoaGhpwdXVFamqqoodE9CEkMBG0ZmZmhqioKNy9exfp6en44IMPMHPmTEm7g97QmJBuzpw5g6CgIGzcuBEZGRlwcHCAj48Pnj59quihEX0E2WMilI6BgQFiYmLw+eefY/DgwTh58iQ+//xzAEBeXh5GjBiBpKQkjBs3TsEjVU6urq4YM2YM9u/fD+Blooa5uTlWrlyJ0NBQBY+O6AvIjIlQGkKhEKdPnwafz4ebm1uvbUyoSM3Nzbh7926b76mKigq8vb3J95SQm36KHgBBvMuDBw/g5uaGpqYm6Ojo4Pz583j//feRlZXVaxsTKkp1dTWEQmG7jflI/yRCXkhgImhv2LBhyMrKQl1dHc6dOwc/Pz/cuHFD0cMiCEJGSGAiaE9dXR02NjYAAGdnZ6SlpWHPnj348ssvJY0JpWdNvbExobwYGhpCVVW13cZ85HtKyAvZYyKUjkgkgkAggLOzM9TU1HD16lXJc/n5+SgpKYGbm5sCR6i81NXV4ezs3OZ7KhKJcPXqVfI9JeSGzJgIWlu7di2mTZsGCwsLNDQ04OTJk7h+/Tr++OMP6OvrY+HChQgKCoKBgQH09PSwcuVKuLm5kYy8bggKCoKfnx9cXFwwduxYxMbGgs/nY8GCBYoeGtFHkMBE0NrTp0/h6+uL8vJy6Ovrw97eHn/88Qc+/PBDAMDu3buhoqKCWbNmQSAQwMfHB99//72CR63cvvzyS1RVVSE8PBwVFRVwdHREQkLCawkRBCEr5D4mgiAIglbIHhNBEARBKyQwEQRBELRCAhNBEARBKyQwEQRBELRCAhNBEARBKyQwEQRBELRCAhNBvENUVBQYDAYCAgIkj8miQeHNmzcxY8YMmJqagsFg4MKFC22epygK4eHhMDExgaamJry9vcFms7t1TIKgIxKYCOIt0tLScPjwYdjb27d5XBYNCvl8PhwcHHDgwIF2n4+OjsbevXtx6NAhpKSkQFtbGz4+PmhqaurWcQmCdiiCINrV0NBAsVgs6sqVK9TEiRMpf39/iqIoisfjUWpqatTZs2clr83NzaUAUElJST1ybADU+fPnJf8WiUSUsbExFRMTI3mMx+NR/fv3p06dOtUjxyQIuiAzJoJ4g+XLl2P69OltmuYBUEiDwqKiIlRUVLQ5pr6+PlxdXUkDP6LXIbXyCKIdp0+fRkZGBtLS0l57rqKiQu4NCsXv214DP9IUkehtSGAiiFc8fvwY/v7+uHLlCjQ0NBQ9HILoc8hSHkG84u7du3j69CmcnJzQr18/9OvXDzdu3MDevXvRr18/DBkyRNKgUJosm+mJ35c08CP6AhKYCOIVkydPxoMHD5CVlSX5cnFxwbx58yT/Le8GhUwmE8bGxm2OWV9fj5SUFNLAj+h1yFIeQbxCV1cXo0aNavOYtrY2Bg0aJHlcFg0KGxsbweFwJP8uKipCVlYWDAwMYGFhgYCAAGzZsgUsFgtMJhNhYWEwNTXFp59+2uVjEgQdkcBEEF0giwaF6enp8PLykvw7KCgIAODn54e4uDiEhISAz+dj8eLF4PF48PT0REJCAtkHI3od0iiQIAiCoBWyx0QQBEHQCglMBEEQBK2QwEQQBEHQCglMBEEQBK2QwEQQBEHQCglMBEEQBK2QwEQQBEHQCglMBEEQBK2QwEQQBEHQCglMBEEQBK2QwEQQBEHQCglMBEEQBK38PzHmYubQL/WYAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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xMcHVV1/N0NAQTqcTh8OB0WjEbDbL2mGo9C4mmH+fKOHLB8p784lnRl6/aB67ceNGWlpaVPNYmVGFKQ2Ew2G6urokM9Xq6uplzVQhPfZA4rlynBnr/FE6uuTkOC8UCtHX14fH4yEYDC7ZQajRaKisrKSyshK/34/T6eSVV16huroai8VCaWlp0tcinqME4s8u8rxU+vKJZyodlYRCoSXTr5HmseJ7oL29XXKVKC4uVtN8caIKk4JMT0/jcrlwu91A7GaqkB4XBkguYkrUv241RUyBQACfz0dXVxfZ2dnSgHMsN87i4mI2b95MS0sLbrebo0ePUlxcjMVioaqqKuFrU/LGvZgwiaTClw/Sm8pbDp1OR319PXV1dZJ57L59+ygvL8dqtVJTU6Om+WJEFaYUIwgCY2Nj2O32hM1URdIhTIlETMnMH62WiGl6ehqv10tPTw9FRUWsWbOGioqKhMQkPz+fNWvWYLPZ8Hq9nD9/nqysLMxmc8YvvFtOmETk9OWDzEjlLcdi5rGnTp0iKytLNY+NEVWYUkQ4HKanpweHw5G0mapIpkdMcvnXZXLEFNnQUFVVxdatWykuLpblWrKzs7FarZjNZnp6enA6nXR0dGAymeJKgSk5VxSLMEUiRx0qHam8cDickJhEm8e63W7VPDYGVGGSmbm5OVwuFy6Xi2AwSH19Pa2trbLMO2i1WkKhkAxXGTuxRExy+tdlYsQU3dBQV1fH7t27UzbDEtmVOTg4KDVKGAwG2RslkkVpXz5ITyovFAol1cwQaR4rttar5rFLowqTDChhpgqZFTGlyr8ukyKmcDhMb28vXq+XQCCQEkukla6tqqqKqqoqxsbGcLlc7N+/X2qUKCkpWfTfKR0xJXNWInWoTE/lLYdWq6WiooLy8nLVPHYZ1J9AEohmqk6nk8HBwZSaqUJm1JhS7V+XCRFTZEODeONM99bTkpISNm/eLDXQHDlyRJZGiWRR2pdPPDMTmx/iYTnzWNFV4vVsHqsKUwKkw0wV0hsxyVE/ioV0RkzT09PSa0y2oSFV5Ofns3btWpqamhY0SlgsFurr66Xf12qJmBZjpTpUpnblJcpK5rGZ9h5UAlWYYkQQBKamprDb7Xg8HrKzs2loaFDMTBXSI0zBYFBaqCbH/qOVSEfENDk5ic/no7+/X/aGhlQR2SjR3d2Ny+Wio6ODxsZGQqGQYjeyVKbVlqpDBQIByQlFKZabY5KLSPPYyclJPB4PBw8epKCggKampteVeawqTCsQbaZaVlZGdnY2NpuNmpoaRa9Fp9MpIkyR9aOxsTEKCwsV23+klDCJrhtzc3N0dHRIDg2rzZRTq9ViNBoxGAxSo8TIyAgzMzPU1dWl/HemRFotug518uRJTpw4gdFoTLkvn4iSUZpGo0Gv17Nu3TomJyfR6XS0tbVx8eJFTCYTZrN51b1P40UVpiUQzVRdLheTk5OSmWphYSEnTpxIiyN1qiOmxepHer0eQRAU6wRLtTCJdUGPx8Pc3BxarZY1a9ZQXV2dsjOVILJR4ujRo8zNzfHKK69QU1OzbKNEsiiZNhTrUBqNhs2bN9PX15dyXz6RdKQPYf7nW11dzZYtWyTz2M7OTurq6rDZbIq476cDVZiiWMxMdfPmzQuK+zqdTvG2bUidMC1XP3I4HMzOzsp+5lKkSpiCwSA+nw+v10tWVhYNDQ3U1tZy9OjRK64LKicnh4qKCmpra3G73Rw5ckQyGa2srJT1Rqb0LiaYF4mioiJqamouq0OZzeaUNKqEQqG0CJOYQlzMPHb//v1XrHnslfWJTJBIM9W+vj6Ki4tpbm5ecrZAqZRaNHILUyzzR6leexGN3MIkTt6Lrhutra2X3ZyvtH1MolgUFBSwdu1ayVHi7Nmz5OTkSI4SctzI0rG9Fv5iahxdh+rs7KStrU02Xz6RcDiclvpO9LkrmceazeaYLM4ynde1MC1mprp9+/YVp7HTMegqnpusMMU7f6TkokDxPDmEYnx8HI/HQ39/P5WVlZLrxmLnXenk5ORIJqPd3d2XOUok0+qfju21wGWimipfvshz0xkxLcZi5rEdHR0YDIZVbx77uhSmmZkZKV0XDocxGAwxm6nC6kzlJTp/tJoiJjHy9Xg8jI2NxTz0e6VGTNHodDoaGhowGo0MDAzgdDqx2+3S6o1ECurpEqblXPgXm4eqrKzEZDIlXIfKRGESuRLNY183wrSYmapoBRJviJ7OVF68bbLJzh+thohJbGjwer3Mzs5iNBpZv359TGmc1fpEmQyRN+/R0VFcLhf79u2jtrYWi8USV6t8ulN5y7HUPFQidah015hiIdo8VnzN2dnZ0o6o1WIee8ULk2im6nQ6GR4els1MNRAIyHylsZ0ba6Qml39dJkdMwWCQ7u5uvF4vWq02YRuo10vEtBilpaVs2bKFqakpXC4Xhw8fprS0FLPZHFOjRKak8pYj2TqUIAgIgpCWGlOi81Pia7bZbJeZx9psNgoLCzP6oeyKFSbRTNXtdhMIBKirq6OlpUWW/n+dTsfMzIwMVxkfK6XyUuFfl4kRkxgF+nw+9Hq9tPU3UdNYFSgoKGDdunU0NTXh8XikRgmLxbKs63empfKWI9E6VCJiKAfhcDjpObHFzGP/+Mc/Zrx57BUlTKKZqsPhoKuri/z8fKktWM6nnUzrykulf10mRUwTExNSQ0NFRcWSDQ3x8nqOmKLJycmhqakJi8WywG3BbDZjNBove08pbagqx0BvvHWodAoTIMs4w2LmsceOHSMvLw+r1UpDQ0NGjU1kzpUkQTgcpr+/H4fDoZiZaiY0PyjhX5fuiEkQBOlJb3R0lLq6Oq666irZBn7ViGlxIhsl+vv7cblcdHZ20tDQgMlkkjIP6YiY5Hx/x1KHEj/r6Vi1Ife5i5nH2u12Ll68SGNjI1arNeWen7GwqoVpfHycZ555hsbGRkpKShQzU013V56c+49iOTMdEVMoFGJgYACPx8Ps7CwGg4F169bJNpcSiRoxLY1Go6GmpoaamhpGR0dxOp288sor1NbWYjabFXf6TlWEtlwdSkwTp0OYUnlutHms2+3mD3/4Q0aYx65KYeru7uahhx7iv/7rvzAYDHz5y1/m6quvvqLNVAVBYHJykvHxcU6ePCnb/qOVUDpiEgX/0KFDaLVaySg3VYVnJV24lRLAVEUxpaWlbN26lcnJSVwuF4cOHSI/Px+dTqdY5JRqIVysDmW324F5s18lfPlElDCOhcvNY91uN4cOHcJsNrN+/fq0iNOqEia3282nPvUpfvzjH7N582YKCgo4deqU4t0ySkZMkfWjYDCITqdj165dirV9KhUxzc7O4vV68fl8ADQ1NUm+aKkm1d58vb29uN1uAE6ePInFYqG0tDRlZ6aawsJC1q9fT3NzM2fPnmVwcJADBw5gsVhSvrdKqXmiyDpUd3c3Z8+elWUeKh6UEiYR0Tx2/fr1VFRU0NbWxvr16xU7P5KkfsNf+9rX0Gg03H///dKfzczMcPfdd1NRUYFer+fWW2+lr68v2esE5iedKysrOX/+PN/85jfTNlugRPPDzMwMnZ2dvPrqq/T09GCxWFi3bh1ZWVmKziKkOjqcmJjgwoULHDx4kKmpKemDILen21Kk6oxgMIjb7ebAgQN4vV7Ky8uB+X1KR48e5ciRIwwMDKREFJWKzHJycqisrKSyspKGhgY6OzvZt28fTqczZWsp0rG9Nj8/n9zcXK677jr0ej2nTp3i4MGDdHd3p9xUOV1rLgRBIDs7e/Wl8o4ePcq//Mu/sGnTpgV//pGPfIRf//rXvPTSS5SUlHDPPffw9re/nVdffTXpi62treXJJ58E5s1Wp6en0+Jhlcrmh+XqR6Ojo2nZYCv3jS66oaG2tlZybhdvaErWfeQ8a3Z2VmplLywslJYNdnV10d/fz5o1a7DZbFJLtrhSW+5IQ8kUpThD1tDQQH9/v+QoITZK5OXlyX6ekohRmlK+fCLpFKZ0ng0JCtPExATvfve7+bd/+ze+9KUvSX8+NjbGc889x4svvsib3vQmAL773e+ydu1aDh06xO7du+W5aubz3eFwmMnJScWXusmdyot1/ihdG2zlOlPsnvR6vUxPT2M0Gi9raIhcn60Ect3Ap6am8Hg89Pb2UlZWxqZNm5ZM14n7vMxmMz6fj46ODjo6OqSW7GRvCOnaYBvZKDEyMoLT6VzgKFFUVJT0eZmwvTbVvnwi6coIwXzEn8728YROvvvuu3nb297GTTfdtECYjh8/TiAQ4KabbpL+bM2aNTQ2NnLw4EHZhQnmIwylhUm8WSd7A4h3/igdwiRHxBQMBqXXCdDY2LhkQ4PSwpTsWePj47jdbgYHB6murmbHjh3o9fqY/q1Op5O6ovr6+nA4HNjtdslcNRUdiHKz1GegrKyMsrIyqVHi4MGDlJeXY7FYkqrPpCOVt5QYpsqXTyTdEdOqEqYf/OAHnDhxgqNHj172d729veTk5Fz2pFhTU0Nvb2/CF7kYBQUFaLVa/H6/rN83FsQ3S6JpxETnj1ZbxBSZ1srPz6epqYmqqqplP6yrIWISU5Fut5uxsTFp+22iKSutVktdXR21tbUMDQ3hcDhwOBwYjUYsFkvc3zddEdNiiI0SoqNEsnuT0pHKi0Ug5PTlE0nXqg1YZak8r9fLfffdx+9//3tZ88aJoNPpyM/PX1XClOz8kVyRWjyIEVM8Z05OTuLxeOjr66O8vDyu15nJEZMgCNIW0ZmZGYxGIxs2bJCtGUWj0UjNBGNjYzgcDvbt20ddXR0WiyXmSExJYo1gcnNzaW5uxmq14vP56OzsXOAoEevTeSak8pZDzjpUOsVhVaXyjh8/Tn9/P9u2bZP+LBQKsW/fPr797W/zu9/9jrm5OUZHRxdETX19fdTW1sp20SL5+fmMj4/L/n1XQnyThkKhFW9KkfUjv9+f1PyReK6SwhTrmYIgMDo6isfjYWRkZMEq+nhJ9Xr16LNWIhQK0dvbK61JaWxspL6+PqU3jZKSEmlmyOl0cuDAASorK7FYLJSVlS37bzMpYopGTF82NDTQ19eH0+lc4Cix0gNvJqXylkOOOlS6U3npTCXHJUw33ngjZ8+eXfBnt99+O2vWrOHBBx+Ulo69/PLL3HrrrQC0tbXh8XjYs2ePfFfNX6w10hExidPYyzVARNaPQqEQDQ0NSfvXiR8OJZ8axZvAUmeGw2HJoWF6ehqDwcCaNWuS2qKppDDB0hFTMBikq6uLrq4usrOzU2L5tBKFhYVs2LCBpqYm3G43x44do7i4GKvVqlhL/XIkmlrTaDTU1tYucJQQo0Oz2bxko0SmR0zRJFOHSrcwrZqIqaioiA0bNiz4s8LCQioqKqQ/v+OOO3jggQcoLy+nuLiYe++9lz179sja+CCSn5/PxMSE7N83FpaaZUqlf11kpKakywVcfvOObmgQHRrkuK50R0yRw756vV5q+U6nCOTl5dHa2orVal2wJt1qtV5Ww8jkiCkajUYjNUpMTEys2CiRqTWmWIi3DqWm8mTk8ccfR6vVcuuttzI7O8vNN9/MP//zP8t9DDAviumImODyWSYl/OsioxeliK75RDc02Gw22a3z0xUxTU1N4Xa7pdqYXO7lcpKdnY3VasVkMkk1jPb2diwWCwaDQbqZKClMcqHX69mwYQPNzc0LbtwWi0XawrpaUnnLEWsdKpZSQaoQXWbSRdLC9Oc//3nBf+fl5fHMM8/wzDPPJPutV6SgoCCtEVMwGGRgYECW+lEsiCnEdAjTxMQEfX199PX1UVZWllLjWKUjppmZGc6ePcvQ0BDV1dWL1sZ8Pg12uxabLYzBkH7T10j3b7HVvLOzE5PJpOj7IxUL9MRGCYvFgs/no729nba2Nsxmc1pme1KVPlypDpXOdFo6RRFWmVdeNOkSplAoRCgU4tKlSwCy1I9iRUlhEtfRA5w+fZra2tq45nQSRQlhEgSB4eFhRkdHCQaDGI3GJVu+X3ghm+/de5YCYZILmvV89ukibrtN+Q3GixFZqxkaGsLpdDIxMUFXVxdFRUWyLMZcjlSmDbOysqSZLtFRYnx8nMLCQmZmZhTrDE51XWupOlRWVhY5OTlpWV+/qmpMmYZer2dyclKx8yLrR+FwmLq6OpqbmxV9glNCmMS2aI/Hw9TUFBqNhi1btqzYDSYXqRQmsVnD7XYzOztLTk6ONC+0GD6fhg9/OJf/Eh7j//ASHxG+xX333c+NNwYzInISiWw137t3L4FAQFpPIZfrwmIocdPUarWS+J4+fZrR0VFF2+hDoVBSzTzxEFmHOnz4MD6fj9HR0aTmoRJh1afy0klhYaEiEdNi9SOXy4Ver1c8rZBKYRI7CT0eD4IgSA4NBw4cUPTpKRXCFPna4C/uExcvXkSn0y1I1VHchX3Ejq3Mht3eSDiswYoDADs2QiENDocWg0H5nVyxoNVqsVqtFBQUSM0EFRUVWK1W2R8ulJ6py83Npba2FqPRiMvl4sCBA1RUVEht9Km4lnR0Aubl5ZGbm4vNZiMcDqfcly8aNWJKgqKiIoaHh1PyvVeaP+rq6krLevVUOJvPzc1JbdHiquXITsJMWq8eL4FAAJ/Ph9frJTc397LXptFo+NV/ajB/7QOE0fI3t7Rw3a1f5OI4HBrW8vkdT6HhLtZxAYAuDGi1Alar8r/7eNBoNAtcF1wuF8ePH0ev12O1Wld04IiVdGywzc7OlholREeJEydOUFhYiNlslhol5DwzHZ514ixRdXV1yn35IlFTeUmi1+uZmpqS9XvGOn+UKevVk2FychKv1ysZj27YsGHRp850r1dPhNnZWTweD93d3RQVFbFu3bpFZ0b6+3M4+bUzPMhLeIth41/De0ywbwAODIX57LH7MBRvocA/DYAbE5m+8DZaLHJzc2ltbcVms+H1ejl//rw0l1VXV5fUTVfpLrno8yI73Lq6umhvb5ccJSK7FJM9M13CJKbTUu3LF30uoApTohQVFclWY4p3/kiJnUyLkawwiQ0NHo+H4eHhmIxHV1PEFGmHVFFRsWjLt2/cJ6Xqurry+TG3EkZDZ7lA/WsNld0z8/8/LIQoLz8Kfpglm2EqQSCjU3lLkZWVhcViwWQy4fP5sNvtC1zNE7kRpaMwv9hnMisrC7PZTGNj46KOEsnUiNI1T7TUuanw5Ys+F1RhShi9Xs/09HRS3yPR+aPVFjFFNjRMTk5iMBhobW2N6QObjhb1eIVpbGwMt9vN8PDwsnZIL5x9gfv/cA+CAIJGy4caHkbQfo6usJHmYS+G15rYul97W2k1OsqHKwC4yDoAdLrMTuWtJBbiynqx1Vzcn9TY2IjJZIq7fqF0xLTcjTfSEHd4eBiXy8XevXupr6/HbDYn1CiRCRHTYqRqP1QwGESj0ajND4mSaMQkh3+dTqcjEFC+ZThekYherdHQ0EB9fX1cT0NKD7zGep7Y8u12uxkfH19RbH3jPj7z53t4cRf0zcC9p8L8s+dL3PeZt9H9eQPnbV7qX+tA9k2DFg1P3fQkRT/vg5MwSikg8I53ZFZHXqJEtpoPDw/jdDrZu3cvBoMBs9kc0+dB6Zt2rKlDjUZDRUUFFRUVjI+PJ9UokU5hiuVcufdDpdv1AVa5MJWWljI9PR1zOkFO/zqdTsfs7Gwil50UsQqT2NDg8/mkLamJWiNlWsQkLhz0eDzMzs7G/Hu0j9j53waoyp3/vwea4VsdYda94Qwlt5Rw9zb4n9fuxYOzQFjgqiMhtCdfBKAYP6Dhhz/M4jOf0WSsOMWbXou8ifv9fhwOB/v376empgar1bpsq7mSDyziefG+h4uKiti4cSPNzc243W6pUUJ0lFjpZ5Wuod54h5flqkOle+UFrHJhKioqiimVlwr/unSm8pY7d2pqCq/XS09PD6Wlpaxfvz7pNtp0ND8sdt5SLd+xfohsZTaed2vZUhpmYwn8dR3YJ6BR34jXkou1GDQaEIT5iCmshfGnP0Iu8z6PBUy9dh2Z3S6eDMXFxWzZsoWpqSmcTqfkWye2mke/j5T2rksmehH9Bm02G11dXVy6dElylFhuc3A69iKJ7/9Ez02mDiVGTOn0hlzVwiRGTEuRSv+6TGt+EBsaRFsdOR0a0p3KCwQCUju7ONuRiD+focjAUzc9xUf+cC8v7hKozIX7mkGv81BW1UrWa0dOBCEE6AQNLYNhajkMQAVDgIBOx6quMcVCQUGB1GoeGWWI7faRHorp7MpLhMhGid7eXqlRorGxkcbGxstSwelI5YkPn8kKYiJ1KDViSpLi4mJmZ2cJBoNSGkfO/UfLkQnND+Jr9Xg8TExMYDAYktqkGsuZSiDeeGZmZvB6vXR3d1NcXLxky3c83LbxNm4034hj6DgM34GGWSYnP0Rz45d56LfAduidAa1Gy5M3PkHNhVfIeuklBKCKQWx0susd5iXTeJniqSeXWOTm5ko3Nq/Xy4ULFyTT2Pr6+rQIk1wiodVqqa+vp66ubtEam1ibSZcwid6YchBPHSrdM0ywyoVJnGL3+/2UlpbKvv9oOXQ6XVpTeT6fT1pc19DQwObNm1P2ZlK6XTwcDuPz+Whra6OiooKtW7dSXFws2/c3FBkwFBmYqPwVbW1vAQKca/gSea+5Egm8VjvRarE/8q+ceknHO/kBAKfYzJkXtyIM15BnqSVcX49QV4dQV8fPDhq450uNjFOEoNXx1FOz3HZbgEAgwPDwMIIg0N/fL9tw61Kk4ncV2Wre3d2N0+mko6ND8TRvKlKH0Y0STqdTqs1YLJa01JhSFbXEUodSmx+SRJxP+fjHP45er+e9732vYsvc0pHKm5ubw+/3Mz4+LuWL5Z5yXwylUnliy/fY2BglJSVcddVVKXNqB9Drd5Gb+0lmZ79CUOMn743zf16UBQIC9/3hPp7b+Bbu4D/4e14iixB6prmaA/Dby7/fu1/7vy4MNIS7uO++XJqa7AQCTillcu7cOakZJVXeZ6mMYrRaLUajEYPBQH9/P2fOnOHixYtMTU0lPS8UC6ke6C0qKpJqM263m+PHjxMKhRgZGUGv1ysWHSqRTluqDpWfn5+WLsRI0nt6Epw4cYI77rgDAIfDwS233MKOHTsUMzpUMpU3NTVFW1sbBw4cYHZ2VrppJzu1HyupTOWJ6cjjx49z6tQpCgoKKC8vp6amJqWiJJKT83ays98OQMFrj2mFr/3/kBBCU9GJoNXxVR4EYB9v4B80L9H3qUeZvf9+Au94B8HrrmPC0Mws8+Izh7hPR0N7e5jNmzdjNBrRaDRcf/31NDQ00NHRwf79+6U2/tWGRqORfkcWiwW/38/evXu5cOGC7G4skSjVbCE2Slx//fUA2O12XnnlFTwejyKfeyXrPGId6vrrr8doNDIyMqLWmOIlHA5z8803c+DAAW6//Xb0ej1f+MIXuO666xS9DiVSeWJDw+DgINXV1Wzfvp2RkRH8fr+ief1UpGvC4TB9fX14PB4CgQBGo5FNmzaRnZ3N2bNnFd3H5Bc+wOj0T6l7bbi2OBtM+eCZ1nBVk4WnnprlpXvfwmeEr2DGzU+4lc01drbe2IatzIahyIDPp+GZdd/laeHDnGETMD+I++Y3WygtFRgfH3/tz3Q0NjZiNBrp7e2V9ihZLJaE3ReiUXqDbXFxMU1NTfj9fpxOJ/v376e6uhqr1SprChaUr/eIZ+3Zs0eqQ3V0dCQ8jBwr6UgfinWo8fHxtO5iggSF6dlnn+XZZ5/F5XIBsH79eh555BHe+ta3AvNF649+9KP84Ac/WLDFtqamJukL1mq1fPjDH+aHP/whZWVl/OQnP5E+9EqSqlRedENDfX09e/bskRoaxsbGFH/ClrPGFAqF6O7uxuPxoNVqaWxspLa2dsETmtJdgL3ThzFEZaD+eRuc9QuM9X6GXbta2P/WcoYHoHzAS13dl/jM0GfR/FhAo9Hy6BseZU/eHq671gf7YJZcdDqBJ5+cXbIBIrLw3t/fj91ux263S91i6b4xxIMogsXFxWzevJmpqSlcLheHDh1ackV6oijtzRdpzyP+vsS9V06n87JGCblIR4u6SDAYTPker5VISJiMRiNf+9rXaG5uRhAEvve97/G3f/u3nDx5kvXr1/ORj3yEX//617z00kuUlJRwzz338Pa3v51XX31Vlou+5ZZbpP9dUFCQFmESU3lyPZ2GQiF6e3vxer0Eg0EaGhqkCCL6XKWFSY6IKXLgNy8vj+bm5iWbAJQtqM9h0P4HOVo4NASbSyA/az6tt6scpv0/BuCDH4czr/2L7/MI4df05u4TYT7xyif45Y2/5K/eWgr74Lo3BDn3b5MxdeWJKbHq6mqGhoZwOBw4HA4aGxsxm80J1WyUjJgWE4qCggLWrVsntZqLKdpYB1qXIx1zU/CXyCly71Vk80BVVRVms1m2tSLpbNletV15kcIA8OUvf5lnn32WQ4cOYTQaee6553jxxRd505veBMB3v/td1q5dy6FDh9i9e3fyVx1Bfn5+2iImSP7JJnJGJycnB5PJtGxDQzqEKZmIaXp6Wmr5LikpiWngV8mIaWbmKXJ0LsYm8vn6pWmshfDYZtBq4F/s8MEt7yc/CH/+cx/bKg+QVTOGRj//9zDvIHFpIgzlSA8RlVUC+jhbxSNveCMjIzgcDvbu3SstMUz3E+xSLCeCOTk50op0caBVbDU3GAwJCUy6LJAWO1NsHhAdJY4dO0ZRUZHUgJWMAKdTmNK9JBBkqDGFQiFeeuklJicn2bNnD8ePHycQCHDTTTdJX7NmzRoaGxs5ePCg7MJUWFi4KoUp+oYd64xOuiKmeOtpExMTeDwe+vv7qaysZNu2bTHXG5TrAvz/mJv7LwDMXyznUzofD74ZTozCjjKoyYMbf/0CT930FIWFH+D/3jnAviIz6x4Q+PJm2FIKFbmg0+iwllqBi7JcV1lZGdu3b5fsgcRNtFarNaahaaVrTCsJReRAa09PjzTQajabaWhoiOvpPB1rNlZ6ffn5+axZs0ZylLhw4YLkKGEwGBK6P6gRU4KcPXuWPXv2MDMzg16v52c/+xnr1q3j1KlT5OTkUFpauuDra2pq6O3tTfZ6L6OgoEDR9eoi4ps1FArFVQ/w+/14PB4GBgaorq6O64YtnpuOiCkYDMb0taOjo7jdbkZGRqitrU2o5VsJYQoE+nG57gKg7Hwrm4+0UUUdX3C/wKkvvJkdZfOWRd/uDPPhP3yY/9hWxgOPm9Hcb+SpX3g5bJ4XJkuBhs/teBJDkUH2axTtgSYnJ3E6nRw4cICqqiqsVutlqzzSRTwiqNVqMRgM1NfXMzAwgMPhwG6309DQEHPaUulUXjxNCOKOK5PJtECARUeJeBolVGFKkNbWVk6dOsXY2Bg//vGPed/73sfevXvlvLaYSFfEJIb3sUQSgiAwNDSEx+NhfHyc+vp6du/enVB6JhNrTNENG0ajkTVr1iQ805LqJ2JBEHC5PkQwOEDWnIEND7QBEHr2KT5VGuBrdrjDClla+DsD/MQXJlgcxGrdxM/4O+47+TTrXiph9mNjWMe3YS27nXkTI+kAWa+3sLBQ2tbqdDo5cuQIpaWl2Gy2Jf3rlIyY4j0rcsgzMm25UiOBIAhpqTHFKxCRAjw4OIjL5VrQKBHLg1q6hEkQhNU9YJuTk0NTUxMA27dv5+jRozz55JO84x3vYG5ujtHR0QVRU19fH7W1tUlfcDR6vT4tEROs3JkXDofp7e3F4/EQDAYxGo1Ju1FkUo0puuVbLgeKVEZMvnEf7u4nyJ34/xCEXFo/E0I3B4H3vIfSd9/MLSNuHm7T8lNfmFuN8LY6+Hm3hsbaRmqr4SN8mPt4mnVnJzgJ2EyXMJuDgGbeATaF5OXlsXbtWmw2m+Rfp9frsdlsVFZWpsV0M1kRFNOWkY4L1dXVWCyWy6LC6EYEJUimpqXRaKiqqqKqqkpqpX/llVek1xedVYokHe3ikWenW5hke+XhcJjZ2Vm2b99OdnY2L7/8svR3bW1teDwe9uzZI9dxEoWFhUxMTMj+fWNhqYgpEAhI+188Hg+NjY1cffXVmM3mpNuA0+HRFy2GwWAQj8fDwYMHcbvdNDQ0cPXVV2MymWR5Q6dKmF44+wJve3EtGv+/APDqz0xUHellrKiW8c9/HrvdjuuMiwdaHuAFt5bpEFgK4Z8sAjf+4EZeHv4eH/m2ETeNFHWFCM5lk58/ySuvdMl+rcshNhXccMMNVFdXc/bsWQ4cOEBPT48UVWRyxLQYouPCtddeS25uLkeOHOHo0aMMDQ1J7wXx/2dajSkWxFb66667jry8PI4ePcrhw4fp7+9f8qFPTeXFySc/+Une+ta3SsNYL774In/+85/53e9+R0lJCXfccQcPPPAA5eXlFBcXc++997Jnzx7ZGx9g/g3t8/lk/76xEB0xiQ0NPT09FBUVsWbNGioqKmT9IKUrlScIgtTy3dXVRX5+/rIt38meJ/dr9I37eOiP9/Cj3Uit4Z8ra+f9xXDn+Hf5P3vtNDXlsXnzZq4vvZ639byNY+ffyI4yeGstPOsIc98f7uMPf30TdmyYwh6yXdkILQFefPEi113XiFnWK16ZrKwsrFYrJpOJrq4u2tra6OjoAJTbkyS3CObn5y+ICkWbHHHtBigbMcmdUhNfX1NTE16vl/Pnz0vNIfX19dJZ6UrlhcNhwuHw6hSm/v5+brvtNnp6eigpKWHTpk387ne/481vfjMAjz/+OFqtlltvvXXBgG0qKCwsTFsqT4xeIhsaqqqqZDcdjT5TaWEKBAKMj49z4MABSktLZV0fshipiJjsI3a+smF+RikswJMdENLCH8sN/Nb/V9xRNMiWLX+piU0GJvlJ13x3njizFBJCXPjNz/gABwCotk/R1wJm81kcjrf+RZgUXp6n0+kwmUw0NDTQ3d3NuXPnOHz4MBaLhYaGhpTe4FIVnS3Wai6eo2Q0kar29OzsbKxWK2azme7ublwuFx0dHdLvMV3CFDlQnE4SOv25555b9u/z8vJ45plneOaZZxK6qHgoLi5OqTfXUgiCQDgcxm63MzMzk1RDQzyI9R4l0jUTExO43W76+/vJyspi+/bty24zlYtUREzGHB+FpfP/+3kX9M6CLgwfG/45Wq3Ahg15zPuKz2Mrs3F6TENIECjJgYocGJ3TcMs3PkUeAmFAb4c+oKnp9Px+prb538fggIYxn/IbbkU3iXPnzklP5Kl2k0j1+zCy1dztdtPW1sYrr7witZqn2iEj1XNTkaa4g4ODOJ1OHA4H2dnZy9agUoXYfZtuYVq1Jq4iRUVFigpTOBymu7ubI0eOMD09TUFBAddccw0tLS2KDEGKH5JUmqqOjIxw+vRpjh07RlZWFjabjYKCAkVECeSNmHzjPp5/+b/pcX4SgJ92wX945kVJ/8vHGJrYzoMPOi4TEUORgf/d+i68r721ri6H95wUaBgTuHTVu3mOO9Db5//uqqtOYzAIHDo8/4R79ECQ9esLeeGF9NkKVVdXs2fPHjZt2sTg4CB79+6lvb2d2dlZ2c5Qsp6l1WqlodUNGzYwMDDA3r17aWtrY2ZmJmXnKjXQKzZKXHXVVVx11VWEQiE6OjqkzmelECO1dG6vhVVo4hpNcXFxTOvVkyUQCODz+ejq6pLMDgcGBigrK1PU1yxSmOQM9cWWb7fbzdTUFAaDgbVr15KTk7NkgTZVyCFMvnEf/3zin3n62NN8bSPk5YBjAr7jAASwDryJJ770IXJy2tDrR4Hqy/79ma7/4v2b5//7vWZ412b48ObP0vzRB7hxbxv698xnDnJynMzd+X9p/uF+ANZznnBYw3335XLjjUFFI6fIBoHIrrBUuEko3YwgikRkq7nT6WTfvn3U19djsVhk96xLR0qtpKSEvLw8GhsbmZiY4MiRIxQXF2OxWFK+yysTXB/gChKmVD25TU9P09XVRXd392UNDSMjI2npkAP5IqbIlnZxwWJ9ff2CUD4dG2yTEaYnjj7BZ1/5LJZCgX/fATY9zIXhixchIAAa6Kj+I57gMa41VeD3z5/lG/dhH7FTOFOJ1/s5nt76l2soygJ08PnvjvHV399H6+QJtOMacgYE5qrgzNCPaC4G/FDIfM0zFNLgcGgxGJRfKBnNYm4SdXV1WCyWmNwkFkP8HSnVjBA9w1RWVkZZWRkTExM4HA7Js07OAeR0bK+FeUEsKCigoaEBm822oFFC3B6ciuvKhBkmuAKEqaSkJCWpvPHx8QWWOlu2bLnszZ6umSI5ajDBYFBy+RYjwKV2WSnt9p3oA0Y4HOYrf/4Kv3U8yk/3QEn2X0aLPJPginybaOD509+nue7/UEABL5x9gQ//4R7eVAUfskFD6fyX/U8v7C6Hshy4pgyeGX4Co+sv32ZkEAqr4Il/gP++WkP1L7/I+MmPAPNrL6xWZd8fK0UxkW4SDoeDAwcOJLyiIl0RUzR6vV7yrHO5XBw5coSSkhKsVmvSXbHpFCbx3JycHGw2GxaLZcH2YNFRQs6MjZrKk4mSkhLZcsyCIDA8PIzH42FsbIy6ujp27dq15KR2OterJypMc3NzeL1efD4fBQUFtLa2rjicmekRUygUoqenh+Mdx5kYf5Qnty78+4EZuPdU1D8S4Ej4X3nLb/6du5rv4s99z/DLq/+yLLBrCi4dhJfLoVAH11XBp2rA8fcGXtHX0rA7D50hwNTUEQqZd4j47x6B/ls+C/b3oZvMX3btRbopLCxk48aNNDU1LVhRIbpJxEI6hGm5syJbzT0eD6dPnyY/P19yNV8NprGR50an1CIbJQYGBqRGCaPRiMlkkmWxZibMMMEVIkzBYFBqREiESAeDubk5jEYjGzZsWPFJRKfTxewhJyeJCMX09DQej4eenh7KysriavlWdg1F7MIUDAbx+Xx4vd55H7Jy8Lxmx3hhDGrzoWcGPnJqPoWXBbylFgz5cEsd6DRwaTxMUdYz3FIDubr5Tm+NBowFYLwRboo4L7cawu/1UYePIBAMQ9X8miwqRRs0bYiHHj3P+64vTYsoxSsW4s3carVKq8SLioqwWq0rPrAoLUyx2hGJrjRiq3l7e/sCV/N4aijpbNte6txIS6exsTHJUaKmpmZRx4x4UFN5MiE+3Y2NjcUtTOKNraurC51OR0NDw2VL65YjHS4M4rmxCsX4+Dhut1syjd2xY0fcNQU5FwXGwkrCFDnoW1BQINX9uie6+cCfNFzwCzgmoey154qAABq0hAnz0Za/rKwA2BYVHIj32LkQjAVBO1jGZGgtxcUdlJYO0Na5EaFwIzs3NeB0lfKc55OU5s4LIQBhHWtrrBkbKS1Fbm4uLS0tWCwWPB4PZ8+eJS8vD6vVuuQOpUxJ5S1F5HxX5LZgk8kUcwosHA4rvrRRHEWJ5T5UUlLCli1bpOWMYhrTYrEkZFOVTvPYSFa9MOn1erRarZR6i4WZmRlp5YRer6elpSWhX2KmpvLElm+Px8Po6GjSM1bpiJgWO0/8vfl8vkUHfQ1FBh6/8Wnu+8N9QIiRwPy/02l03LbhNn7R9l3CwrwwCQI4JuFHXriqHG6sAdckPHwORuZgKjzfUp7/xH6eedqC2fwzRkffTXXjWf7PobOEO7R8Ye1Xedu/5XL/LbOEtEBYh+bX3+Gqd9UROROlJMmKRXZ2NjabDbPZLA22dnR0LFpwz3RhEoncFjw4OCgtYxRdzcXt0HKemQziPSUegYhczuj1ejl79iw5OTmSo0Ssr0GNmGRCq9WSl5eH3+9f8WtjaWiIh1StV1+JpYRJEAQGBgZwu91MT09jNBpZt25dXHb7S52XzohpamoKt9tNX18fFRUVy64KuW3jbdxovhHHqIOCrAKmglNYS60c9h3mu2e/yx/7obkI/tMNfxyYrx99tDUHmOO7v3gvvrrvgzaMLgzv/uW1/Mf4Wq66ahKhaCOj/fNNEHfZ4OnOMJ89/xAuu0DTE+t4a/nTaEebeepLVRgMAYV+UkuTrFhEu0mI0YbFYsFoNKLT6dKSykvmrMj2+dHRURwOB/v27Vu2O3G1CJOI2CghOkqIjRLi73Kl6E9tF5cJjUZDfn7+kkNo8TY0xEOmpPLC4TA9PT14vV5CoRCNjY3U1dXJ9uSTrhqT6DwhpiF37twZ05yKochw2X6kXYZdaNDweIfAXBjEV/PVLTvI0R4jL28t/2fzdzj7qY/yg/IdrBsO8nf+b/H0t+cbGPZ5PPimoTQHrq+EpzshpBHoLIf1n/gcvzbtxmoNLypKoVCItrZJzp+voL5+hA0bUmfnJPcDRGTBva+vD7vdLrlJKO1oLqdIlJaWsm3bNiYmJhbsuop2/U5HaisUCklzaIkiliaMRqPUKGG32zEajZjN5iWzJ6FQaNkIUilWvTDBfAE32mE8HA7T39+Px+NhdnYWo9HI+vXrk44eIklnKi8UCi0o/mdnZ6+4lj2Z85Sc8p+enmZycpJjx45JDxLJDoMaigw8/ean+fDvP0yYMBo0fPnaT7NJ9zSCAHV1H+O220L8g/tXVD8WZNC8lf88vEYSGmuplfHXrPRe9M7/f10YLMZNlL77Zq7VXP4+EH8///ZvYU49NUU3dTzySA0f/3gHH/5woewGv5C69JpGo6G2tpaamhoGBwclgRLNfeX8XC1FKqIXvV7Pxo0bpVbzo0ePUlxcLDV/pCtiEsdCkiWyUWJ0dFQaSK6trcVisVyWeYh38WmqWPXCpNFoKCgokIRJnM/xer1otVppPicVTz3pSuUB9Pb20t7eTmFhYUpczCMRP5ipFCaxLuZyufD7/WRlZbFr166Elw0uRmSaz1pqRTf5I3y+MaCBsrK3QzhM5U/+HwD6j36A3IgGhorscWrz5gd1/6d3XpT+5ZdQ9bkvEor6mQSDQbxeL16vF8E5w6anfs0TfJdx9Py18Gu+8Y1rWb/+TzQ2arHZbCmf5peTyHRYV1cX586dk9wklnsSl4NUrlXPy8uT1qOLzR+5ubmKr3KH1JnUlpaWsnXrVqlR4vDhw5SWli6IftXmBxnJz8/H7Xbz7W9/m40bN1JYWJhwQ0M8KJ3Km5qaklKShYWFbNq0SRGjR/FnmIo6k2iF5HK5mJ6elpwnPB6PrKIkIqb5QqFJzjmeBkCjeQ8ajQ7dn/+E1uFAKC4mcOutC/7d6OivACgqup5f+PJY++LvqN18LdM33CB9TSAQwOv10uX1Uu9yccNvfkPB736H5rXEYTET7ON6Xgr/PVmDX+DkUDG/+10/W7Z42L3buGT3WzwoWfcpLi4mOzubHTt2SG4SqbIGAmXWqkc2f/h8Pi5evEhbW5u06FOJm3aqxSGyUUIU4ZycHCwWC4FAQG1+kIOzZ8/S3d3NN7/5Ta699lr+8R//kfLyckXOViqVJ67VGBwcpLq6mrKyMioqKhRzH07FuoHo7beNjY2SFdLg4KAsZyzH4OB3CQYH0emMhMPz00rZ/28+Wgq84x0QVQgfHf01ALXh6zD/21fQhGDq4YdBo2Fubg6Px0O32431xAlu+u//Jvf0aenf/p438+98gBvYyz/xr/wDP2bmo7/kJ7ydp/kiX9ds4b77zvPXf92J1Wpd0oEj0xAj6JKSErZu3SrVa8QttIm4SSyHktGLTqeTHM2rq6vp6uqis7NTcjVPZepSqaglct7L5/Nht9uZnZ1VI6ZkeOWVV/jKV77Cn//8ZyoqKnjnO9/Js88+q+g1pNIRQUxtud1uxsbGpJbvvLw8zp8/r2gKMTKVlyyhUIje3l7cbjcajUZq1Ii8EafaAikcnqGv7ykA9PoP4vfr0PT0kPWr+ago8IEPLPj6uTkfU1MnAA3V/3IGTShE8KabmNq2DXd7OwPt7bTu28fGX/yCrJ4eAITcXALveAeBD32I9mOb+fGHc/lR+J18R3MX3xQe4CZe5t38F+/gR+wUjvDUU1t5+9sL6ejooLNzXqAS8UNL5/ZasV7T1NSE0+nk0KFDVFRULFjylwzpqPcIgkBlZSUtLS0MDQ1JreapTF0qnU4TRdhoNLJv377VXWP66le/yk9/+lMuXbpEfn4+V199NV//+tdpbW2VvmZmZoaPfvSj/OAHP1iwMLCmpibpCz9x4gQ7d+7ke9/7Hh/84AcVWTkRjRgxyXkzEARBatqYmZlZ1IUiHRZBkJxxbKQ3n7gkrbq6Oi3efIODLxAI9JKdbaSw8H/j9w+S/R//gSYUIrRrF+H16xd8/ejobwDQazdS8Px/A9D+3vfS++MfU/LnX5B96o9k98yR5YdwVRWBO+8kcMcdCFVVANy2LsDatR4OHhzAZNrJm9/7e46ykx0cZ4QyzrKRcFjD9LSBa6+tpaenR2ouENuzMzGCWup9n5+fz7p167DZbLhcLo4dO0ZxcTE2my2pWqgSqbxoRDHUaDRUVlZSWVnJ2NiYbEa4i5GuOo/4uVvVqby9e/dy9913s3PnToLBIJ/61Kd4y1vewoULF6T88kc+8hF+/etf89JLL1FSUsI999zD29/+dl599dWkL/y+++6T/ndhYeFlXXlKIL555BAmMZLweDyEw2EptbXYG1TppguxdTURsZDqLlEuDcv9vFLZnh4Oz9Hb+wQAtbX3Ew7nIASDZH/vewDMRUVLAGNj82m88j8F0AgCQ2vXUvtv/8aBqf18651w7f+Cf3fAt0vew3ve+zgsUhurrQ2yceMQ69cLaLVwfXgvX+ARvs3dhMhGqxWw2eZvvAaDgfr6enp7excIVCzbaNMZMUWTm5tLa2srVqt1gXfdcm4Sy5GORoRIM1WRyNSly+XiwIEDVFZWYrFYZIkM09mAsOqbH377298u+O/nn3+e6upqjh8/znXXXcfY2BjPPfccL774Im9605sA+O53v8vatWs5dOgQu3fvTu7KI9Dr9XR3d8v2/WJFfMMu9uaNlcg9T9nZ2ZjN5hVbvtPlah7PmbOzs/N1l+5uiouL4/bmS1XENDT0IoFAF9nZtVRW3sbAwBjlR46g9XoRysoI/t3fLfj6YHAUv38fALX/fhEB0PdfpGMLhN8J/1w87yLxx364d/y/eOPcpzHkGi47V8RohGeeCXL33QV8LPzN116vwDPPBDEa//J1Go2Guro6amtr6evrk1JIokAt9VSr5CB0rCIoNhSYTCa6urq4ePEiHR0dWK3Wy9K4K52XjohpqRu1Xq9nw4YNNDU14Xa7pcgw2b1J6RIHQRAIBoOrO5UXjTjgKjYeHD9+nEAgwE03/cUGc82aNTQ2NnLw4EHZhWlyclK27xcr4psnkd7/2dlZyV5Hr9fH1fKt1WoVN4+NVSzEzsHe3l7Ky8vZunVr3AXwVAmTIATo7f0WADU1H0arzQPGqP/v+fRc4N3vhoiU8Pj4OK4zT0NekHwvjOyAto/B2EZACy3S9cK9TXDf6RBHuo/wv1v/97LXcfvtYd785jkOHZr/Xe/eLSwQpUgi54cGBgaw2+04HI5l16VnSsQUTeSa9O7ubux2uyRQsZirpqPGFMuZeXl5UmTo9Xo5d+4cOTk5CTeyJPOgmwzig+eqjpgiCYfD3H///VxzzTVs2LABmJ+zycnJuaxzrKamht7eXjmOlVB6vbqIRqOJO5KItNcpLy9PyBYpEyMm0aWhv78/YbNYkVQJ0/Dwj5ibc5GVVUlV1XzKLru7m4rDhwGYu/12YL4Lsu+Pf6T0p8+R89Z9zLXAtBE6/pI9JjtvC0+cP037uMBjm2BTKVxXCbf/5nbG58a5beNty16L0Qh///exv0ZxULKqqoqhoSHsdjtOpxOTyYTJZFJkwDWaRNOGkW4SkeaqomgtFQ2Gw2FF6x/hcDiuKE2snZpMJnw+H52dnbS3t2M2mzEajTFfe7oiJvFhd1XXmCK5++67OXfuHPv375fj28VNcXFxWoQJYp9l8vv9uN1uhoaG4rLXWepMpYVpKbEYGxvD7XYzPDxMXV1dUmaxK52VDIIQoqfnGwDU1NyLVjtvSVX0ox+hEQSC113HSGUlI899k9z2/4e2yYvnHkC8P2hA319B6baPUlb2t+TkNHBD+AV++vsP80NPmPea4TNr4V2Hw3z49x/mRvONl9kiyUFkEX54eBi73Y7L5aKxsRGz2ZxRNaaViExXDgwMSOnKpcRW6VSe+BmL90yxy010NRftgERX85UeIlI1YLsSohXSFREx3XPPPfzqV79i3759GCPyEbW1tczNzTE6Orogaurr66O2tjbZYxeQrogJlm9EEH363G434+PjC1q+kyHdEVNkK7vf78dgMNDa2irbQGwqhGlk5CfMztrR6cqoqvq/APhcQRq//2MCxdB5zShTv2hhfOMsbFn4b3WTsP0uHeHf/BmhxiT9+W0bb0Ofo+eDv3k/72yEbC18vBUePBvm2ZPP8qXrviTra4imvLyc8vJyRkdHsdvt7Nu3j+rq6pSeGYlczQiR0eDIyIgktkajEYvFIn1elE7lJZvaihTeoaEhnE4nTqczJr+6VAyXr4Ro4JoJLiQJC5MgCNx777387Gc/489//jMWi2XB32/fvp3s7Gxefvllbn1tir6trQ2Px8OePXuSu+ooioqKmJ6eVvRpUWSxIdtwOCy5fIs+fRs3bpStqJgO81jRrkR8XVNTUzQ0NMS0UDGRs+QUJkEIR0RLd6PTFfGf/+ln9Jef5n99so+R7YDujPT1hSNVlNb/I4NhH7OzP6bmD5B18+3MmkyXfe9d9buYEzT8l1fgNhOsK553LP/28W9z19a7UhI1RVNaWsr27dvx+/1cunSJYDDI+fPnsVqtKR2jkPvzptFoJLEVW7L37dsnuUmkS5iSPTO61Vxc7Cf61RUVFS34+nSm8jIhjQdJCNPdd9/Niy++yC9+8QuKioqkulFJSQn5+fmUlJRwxx138MADD1BeXk5xcTH33nsve/bskbXxQTwzE1J54opvj8cDIA2Pyv0mUzpiCofDhEIh2tvbF7Syp+pNLPfDhaPneWZmLoFGj05XxokTf0Nz836yPhFk5LWvyenIpkR4I9U3PUze9i288IIOU00D+kooejWb7//tJ/n7Rb63ocjAvdvv5enjT3FtJVgK4Q2V8Lu+MI5RhyLCJFJcXExraytHjx4lEAhIczZWq3XVWQRFtmQ7HA5effVVcnJyFHW+FiNCOd+P4mK/yclJnE4nBw8evGwIOd1bc1d1xCS6LNwQ4RUG8y3h73//+wF4/PHH0Wq13HrrrQsGbOWmpKSEmZkZ2b9vLOh0Oubm5nC5XHi9XnJzc5cdHpUDpYRJXKfhdruZm5ujrq6O5ubmlH9o5IyY/vP0dyif+AQ1eRAKT+D1fhSArCwQ7Hoa/zTNv+79FN/seoT//vUcjVUhfD4N33vmVR7952F0k/DiqQ/x4AkLe26dXHQz7V3b7uLbJ77NEx1hggJc8M8vJ7SWWmV5DfGi1WrZsmXLgpt6bW0tVqtV1kFQJTIUer2eTZs20dTUxJEjR3C73UxOTmKz2VJuyZXK7rjCwkI2bNgguZofP34cvV6P1WpN206kKyJiiuXGkZeXxzPPPMMzzzyT6DExUVJSwvT0NKFQSNEf7OzsLDMzM3R0dFBcXMy6desoLy9P+Yc11cK0mEtDV1cXZWVlinxgRGFK9sbnG/dxzvkJ/qFh/r91mvmttebSe/jAuz+E192CgAYBLTqdgNU6/zO127Xcfc3nASg/DP8ReB8hNDgcWgyGy1OohiIDT930FPf94T5CQgidRseTNz2paLQkEvkzi7ypOxwODhw4IKuHnZKp84KCAgoLCzGZTMzNzXH06FFKSkqwWq0pc9ZXogkhcgjZ6/Vy/vx5AoEABQUFCVlSJYPS98/lyIyrSBLxycnv9yti4Do5OYnH46Gvr4+srCwMBgPNzc0pP1ckVcIUCATo6urC6/WSn59Pa2ur5NDe3d2tWPow0s080RtOOBzmcOdhft8Hf1U7L0iPd4B7Cn79D2/l4x+3cN99WsIhDVqtwJNPzkrRUGGdl8Eb5tOxlfvhXr7NB3X/LgnXYkSv1EiHKC1FQUEBGzZswGazSR52lZWV2Gy2pDY4K13TDYfDkgu21WrF7XZz+vRpCgoKpCyFnNejZE1LfAA0m83s27ePgYEB9u3bF3ereTJkiusDXCHCJH64Ui1Mka3RNTU17Ny5E5fLpfgMidzCFDnsW1xczIYNGygrK1vwIVdyvXoyazbEOp/b7SY7mI19UsvfHPjLz0pMsV17W4Abbwxy6tQE4XA7t9yyFYAXzr7Ao6/ey/d2CgTC8JsZeD/Pk//JezEYbMuevdjmXKVZTixEDzur1YrL5eLIkSOUlZVhs9kSstJRWpgia1rZ2dk0NTVhNpvp6uriwoUL0rCuXO7s6Rjo1Wq16HQ61qxZQygUklrNGxsbUz6vdkWk8jKJ/Px8srKy8Pv9sn9vQRAYGhrC4/FILd979uyR2jnT0SEnlzBNT0/j8Xjo6elZcdhXyfXqiQhTKBTC5/NJ6cempiaqq6t5qnzpFJvBIJCfP0tb23x90jfu48N/+DCPbZw/t3sa7nyLhjefD/MPpx9hhu/L/ErTg7gUTxSoY8eOUVJSgs1miysVnY6IKfq8SDcJn89HR0cHHR0dWCyWmNwkliNdDgxiSq2qqoqamhqGh4dxOBy4XC4MBgNms5mCgoKUnKtGTDIibrGVU5iiV7M3NDQs2vKdjvXqyQrT5OSk5NJQVVUVk0tDpkZM4vpyj8cjWcNELohcKcUWeaOzj9gJC2EKX/tUmArhjTUCHRUajL/8JXNHjxLeuVOmV5ka4hGLnJwcWlpasFgsuN1uTp48iV6vx2azxbRkMx3CtJRQaLVaGhoaFrhJ2O12aX9SIpFAOgddxXM1Gg0VFRVUVFTg9/slV3OxmSW61TwZ1IgpBeTl5ckiTPG2fOt0OsV96xIVJr/fj8vlYnh4mNraWq666qqYn7wyLWKKrIeJGzmXetpfKcUmnmMrs6HVaPn/nQjz8Rb4X3XwYCvUvGs3fOUguZ/7HNO/+tW8OV6GksjDQ2RazOPxcObMGfLz87HZbMvWbdKZylsKrVZLfX09dXV1C7wFE0mFpSOVB0tHLsXFxWzZsoWpqSmp1by8vFxqNZdjw4GS7fjLcUUIkxgxJbP6QrzRdXV1kZubi81mo6qqKqYPgtIRk06ni7lrTRAERkdHcblc+P3+y1KRsZIpEdPc3Jy0RqOoqGjRelgiZ8HC7rpvtIfI0Wq4qUZg8M0nGf1NFqWvvILuj38kdOONib0whUj0Z5GVlYXVaqWxsRGv18uFCxfo7OzEZrMtuqZC6TUU8ZwX6SYRmQpraGjAbDbHdANO12LClSK1goIC1q9fL7manzhxgsLCwqQbQNSuvBRQUFDA+Ph43P9uZmYGr9crrWeIt+Vb6d1I8JdJ9OXewGJtzOVyMTU1hdFoZP369QkXT9MdMUU2aJSUlLB582bZ5lgiz4lM/VlKGpjp/zhjY7/j7Ndz2XpXkPzPf56pN74REtgsqwRynJOVlYXFYqGxsZGuri4uXbpER0cHNpttQWNBJkZM0USmwkZHRyU3CYPBgMViWTZjkI6ai/iQG8u5ubm5tLS0SK3mFy5coL29HYvFklCruZrKSwHxClNknaWioiKh9QyQvuYHWFyYxA244lCsmHdP9g2XrohpZmZG2uuU6BqNWM6KJDL1F9Z/j/b2v2OSQ5x5TMPWe06R9bOfEXzNZmslwuEwPp8Pp9OJIAgcOHCAjRs3ylobiEYusdDpdJhMJhoaGqTGgsi17+kwVU3mvNLSUrZt28b4+HhMtZp0REzxCJOI+CBhMpno7u7G6XTS0dERd31NbX5IAbEK0+joKB6PR2r5jqfOshjpaH5YbNV5OBymt7cXt9uNIAiy2yGlwzjWbrczODhIRUUF27dvT9nNfDnB1WoLaGr6Ee3t/4vp8nOcfgy2fOOz8Dd/A8t4BIbDYbq6unC5XASDQYqKiqisrKSrq4tXX31VWp4o92tKRRQT2VjQ09MjranQ6/WKWwTJIRRFRUVs3ryZ5uZmqVZTWVmJ1WpdEIWnU5iSXSfS398vNYCI9bWV0veZsiQQriBhKiwsXHJZoJjWcrvdTExMyOqGna5UnphaC4VCkkuDTqeLaQNuIiiVypuamsLlcgHzH9Jk1oPEQiw3gKysUpqbf8qli29mxuDm7D0e1n7/XxDef89lXxsOh/F6vbjdbkmQ1q9fLwmQzWajq6sLu90uCdSGDRtkjQJTRfTa9wsXLjAyMoJer49p7XuyyC26Yq3GZrPhcrk4evQopaWlWK1WysvL07aYMFm/Oo1GQ01NDdXV1YyMjOBwONi7d++y6UtBENSIKRXo9frLmh/C4TB9fX14PB4CgQBGo5FNmzbJ+lSQjlQezL/5vF4vfX195OXl0dLSElOLb6KkemuuuGhwYGBAElabzZZSURKJJUWZnV1LS+svaDv5BiZtE7Rf+iz5vdfjmBvBVmajrrBugSCJ9croiEij0dDQ0IDRaJSWyR04cEA2gVKi7iOucxgdHWVqaoru7u4FW3VTVadIlVBEznV5PB5OnTpFQUEBubm5inepySkOkW7tfr9fcjWvqalZ1JZKbX5IAYWFhVIqLzKK0Gq1NDY2Ultbm5KnAaVTeXNzc3g8HsLhMGNjY6xfv16WVtGVSFXEND4+jsvlYmhoiLq6Onbt2kV+fj4DAwOK1LTi+bnl5lppWvNL2s++Cf+aAH88ew2PXIDZkJa7TXdzY9mNSwrSYueKaZfu7m46Ojo4cOCA1GmYjFWQkhQWFrJt2zYGBwfp7OxcsFVXzgdAsQs1lRFMTk6O1Dbv9Xrp6OhAp9NRWloqm5vESqQqaikuLpbSly6Xi0OHDlFeXo7FYpHcctRUXgooKirC4/Hw8MMP88Y3vpHi4mKam5upqqpK6U1bqVRepEtDWVkZ2dnZtLS0KHYDk7v5QXyCGxkZWbSFPVXr1RcjnnMKSrfTt+8uit/8z+woh3/fDu85EuYZ9zPcfu3ttNa1xnW2RqOR0mPd3d10dnZy8ODBhAVK6Q22Ylq5qqpK2qrb2dm5YKuuHDY64u9IidcmNhNMTEwwOzsruUlYrVYMBkNKBSrV6TRx5k9sNRejQ7PZnLaB4sW4IoTJ7XZz4MABTp48ydjYGP/4j//I2rVrFXkTpzqVF9k9WFlZKTUBHDx4UNFITa6IaXR0FKfTKW2+Xbt27aI3LqWEKd73iMcT5lfOcf7xtX9WlQv1eeCbCdMX6KOV+IQp8joWEyi9Xs/GjRszMoKKFsHI1mxx7fvevXtpaGjAYrEkVdOVa2lfPAiCQFlZGdu2bZPcJDo7O5Nyk1gJpeo8OTk5NDc3Y7FYpJEAQE3lyYHdbufzn/88P/zhD2lpaaGlpYU//elPil6DGDHJ/aTq9/txu90MDQ0t2j2odJdcMhGTuIrd5XIxPj4e0+bbTIuYgsEgvrOnGf/Dnbz/rk4A2sbh8+ehZxa06LCVLW/yGguRAtXT00NHR4ckUBs2bFhxdkvpiGmps6LXvkcKVCJ1m3QIkygSkW4SYrebw+HAZDLR2Ngoq7Gq0v58otdgVVUVr7zySsak8pL+Cezbt49bbrmF+vp6NBoNP//5zxf8vSAIPPLII9TV1ZGfn89NN91ER0dHsscC8wXzgoICzp8/zwc/+MG0bF6MdGFIFvEGfurUKU6cOEFeXh67d+9m7dq1l3XSKC1MiURMgiAwODjI8ePHOXfuHGVlZVx99dVYrdYVPwCZEjEFg0EOnT/Ij79xOxMnbmL2TfOidOZlIx86pqVnFgjr+OKup2V1FtdoNNTX13PdddexYcMG5ubmOHToEPv372d0dFS2c5IhFicGce377t27mZmZYd++fZw/fz7ujdPiey+d3nxit9vu3bvZvHkzw8PD7N27l7a2NtkWlaarM058rZmwvRZkiJgmJyfZvHkzH/jAB3j7299+2d8/+uijPPXUU3zve9/DYrHwmc98hptvvpkLFy4k3fGyefNmvvOd7wDzxb3p6emkvl8iiG/cZJ50ItvZJycnMRqNrFu3btknsUyOmERBcrlczMzM0NjYGPeQb7ojpl/t8/KzfecQsn9Be/mLfOpamMmDcACmznyaB776RcKFPrSVHXzxI43cf11dSq5N7ICrra2lt7eX9vZ2Dh06RGFhIRs3brwsgsqUiCma4uJitm7dKg237t+/P66170rWmESW6gLUaDRUVlZSWVkZt5tELGe+3rfXggzC9Na3vpW3vvWti/6dIAg88cQTPPzww/zt3/4tAC+88AI1NTX8/Oc/553vfGeyx0sUFxczNTWluE2K+CZKRCQiXRpmZ2dpbGxk8+bNMb1BMjFiEl+Py+UiEAhgMpmor69P6IOWrogpGAzyxgde4HTjA1SVhbnbCh+qmvdt7Z2Bz53X8sv3vI+LfzOD3V6JzVax6Lr1VFynKFB9fX0LBGr9+vVSZ5VSYi6eFe/DmDjcOjk5Ka19r6mpwWazLetwL0ZnSn62Y3nYjHaT2L9/f1Jr7NMVMWXSDBOkuMbkdDrp7e3lpptukv6spKSEXbt2cfDgQVmFqaioSLZwOh40Gk3cDRCRLg3hcBiTyRS3S4PSg73LRUzivFj060kmV650xBQMBnG73fzk950MtNzPx8zwlhrIfu0lTAbh0+fAMRnGPmLnukaDIoIUjUajoba2lpqaGkmgjhw5IgmU+DVKkMxDoBjxiVt1Dxw4QFVVFTabbdFZLqXtjyC+6CVScCNfj9VqjatxJV0CIUZMV0wqbzl6e3sBqKmpWfDnNTU10t/JRVlZWdx5a7mIVZjkdGnIhIgpHA5L22I1Gg0mk0m2eQ+lhCkUCiEIAvv37yccDnPWN0NLC7zttczciRH4TQ/8eRBCwvwGXDmaHJJlKYHKyckhNzdXkcyBHGdEui+Ia98rKiqw2WxptwdK5MzCwkI2bNhAU1MTTqeTI0eOUFpaKm0JXunnpUZM82ROUjFJSkpKCAaDzMzMkJ+fr+jZK0UvgUAAn8+H1+slNzdXlvmqdNaYIteX63Q6yW5fzhtHqoVJjJC8Xi8w//o2btzIewsC/J/9Gn5ZKvC7PjjvBwRAMy9KT79F3iaHZBEFqqqqira2Nnw+H3Nzc7zyyitJrwRZCTnFLy8vj7Vr10pbdUV7oKamJsrKylaNMImIr8dms0mrKfR6PVarddnPfigUksUqLV4yyfUBUixMtbW1APT19VFX95ficF9fH1u2bJH1LPHpyu/3Ky5MS0VM0buD4l2psdKZSkdMoVAIj8eDx+OR5iBSNcCcKmEKBoO4XC66uroIh8OUl5fT3NxMb28vZ8+epa6igm29z/At7gVtCMI6tg59ia/cswVbmS2jRAn+khZ2OBxoNBo2bNiATqejra2NI0eOSBGJXO+7SFIRleXm5tLa2orFYsHlcnH8+HGKioouy7oogRyt25HzQh6Ph3PnzpGbm4vVaqW2tvayn1+6U3mZQkqvxGKxUFtby8svvywJkd/v5/Dhw9x1112yniXmpcfGxhR/E0fbEkWuaigrK5N1d5CIksIUDAbp7+9nZmaGvr4+1qxZQ0VFRUpTRXILUzAYxOl04vP5JEFav3691BlaXFyMyWTC5XLxyF8budT9P1zsC/C2NzTz1msyS4xgXhR6e3ux2+0A0q4k8XdSVVXF4OAgly5d4ujRo9LEv5y/t1SmC6PXvnd0dCAIAgMDAyn1hIxEzg45cQmjyWTC5/PR1tZGR0cHFotlgZuE0nNMIldcKm9iYoLOzk7pv51OJ6dOnaK8vJzGxkbuv/9+vvSlL0lPDZ/5zGeor6/n7/7u75I9egE6nY78/PyElgXKcXY4HGZychKPx0NfX98Cl4ZUoIQwBQIBKeLLy8sjKyuLHTt2KHJTkEuYVhKkSMSndZPJhMHlYo3PR1XREBMTJQl1WKUCQRDo6+vD4XAQCoWwWq2LNppE2gSJAnXs2DHy8/NZv369LAKlRB1LXPuu1+u5cOECZ8+eJS8vb8W173KQivShTqejsbERo9G4YIWIxWLBaDSqEdNrJH0lx44d441vfKP03w888AAA73vf+3j++ef5xCc+weTkJP/0T//E6Ogob3jDG/jtb38ru2uvRqMhLy+PsbExWb9vLITDYTweD5OTk9TU1KR8VQPMC1MgEEjJ945MQYp7g3Q6HadPn1asaydZYQoGgzgcDrq7uwmHw1RUVLBu3bqY3nei27TZbMbpdHL48GGqq6tjnrlJBWIrvt1uJxQKxbyldDmBWrduXVLRh5KjGVqtltzcXHbv3i1ta43cqpuK60hlXStyhYj4oGG329MWtQSDQcWd1JcjaWG64YYblr2BaDQavvCFL/CFL3wh2aNWROmIaXR0FLfbzfj4OKWlpezevVuxX24qIqbZ2Vk8Hg8+n4+ysjK2bNkitbpOTk4qXtNKRJiSEaRoxAK2KFCHDh2itrY26SHKeBDTV3a7nUAgcFnqJ1YiBWpoaIhLly5x/PjxpAQqFucHuRBFQuxmFbfqtrW1SVt1kx1RiD5PiRb1yO7KoaEhTp48ycWLF5mamsJsNivWCJFJBq5wBXXlaTSauNerJ4IgCAwPD+NyuZiYmMBoNKLRaCgpKVH0iUNOYZqZmcHtdtPT00N5eTnbtm27bJZEybmiRM4TBcnn8yEIApWVlaxdu1aW34l48zabzTgcDg4ePEhdXR0WiyVljTaie4bdbmd2dlYSpGRvHqJrwTXXXMPw8DAXL15MWKCUjJiio5fIlFh3dzd2u10SKDkcwMXPllI3a/H3kpeXh9FolOyOjEZjSt9nIldcKi+TSKUwiU+uLpeL2dlZGhoapKWDFy9eVHxZoBzCND09jdvtpre3l8rKSnbs2LFkLUU8T6mbUazCFAwGsdvtdHd3yy5I0RQUFLBhwwYsFgsOh4MDBw5QX1+fsDHpYogPPna7nampKan2IPcNUnQCjxaovLw81q1bF3O3pVKF+qWiM3GduLhV1263Y7fbk/65pcM0FuabEEpLS7FYLPj9fhwOB6+88kpSbhKxnqsKU4pIhTBFuhqEQiEaGxsvs9lJ13r1RM+MXKVRXV0dU01MvClkijBFC1JVVRVr1qxRJGoVXQsmJiYkWx3RJy2Z1IsoSJOTk5Jzdaqf2CMFamRkhAsXLkgGwmvXrl22wUDJVN5KabVIB/C+vj7sdru0VTeRFRXpFCbxd15cXMyWLVuSdpOIBTViSiGFhYVMTk7K8r2ih0jFLbiLvVHTsV49EWGamJjA5XIxODi46CqNlc4D5bzYlhKmYDBIZ2cnPT09kiCtZHibKvR6PZs2bVrgk2Y0GuOuDYyMjGC32xkfH8dkMrF161bFbxLiGm5RoC5evMjJkyeXFah0pvKWIrJmMzAwQGdnJw6HA4vFEtfa91AopLg3n3hu9OtczE2irKwMq9UqeSTKca4qTCmisLCQiYmJpL5HMBikq6srLpcGnU7H3NxcUufGSzzCFL2+PJEmDfH1K1UkjRamYDBIR0cHvb29CIJAdXX1kksGlUb0SRNTL/v376exsRGTybTs9Ym7ivx+v2Tgm+59OJECJab4Tp48SW5uLuvWrVsgUEoLUzxnaTQaqqurpXkuu90e19r3dDQDCIJAOBxeUiAi3SRcLhcnTpygqKgIq9WadHelKkwpJJmISWyR9vl8FBYWxuXSkKmpvLGxMVwuFyMjIxgMhsvWl8d7HigfMWWyIEUjpl7Gxsaw2+0LBCryRij+/ejoKI2NjVKtMtNYLILKzc1l7dq11NTUpGWNe7xEr32PFKjl1r6nywIJVk4fRg4fe71ezp49S25uLjabjZqamoR+J2oqL4UUFRXR19cX17+JdGkoLS1l06ZNcbs0ZFoqT9wWu9L68niIjJiUIBwOS80mmS5I0ZSUlLBt2zYpIhIFqqysDLfbzcjIiLTFdzW8HnHB4+joKBcuXODUqVPk5uYq+p5Ptp4VufZdTJ2KW3XNZvNlGYR0CJP484w1UsvOzpbcJMT16O3t7Vit1phm3ETUiCnF6PV6HA5HTF87NTWF2+2mr6+PioqKRVukYyXakkgJoqM0cfut0+mUlg2utL48HsR8e6ojpkAgQEdHB/39/cD8TX7z5s1pMbZMFnF7q8/no6OjA4fDQUlJCbt371ZsDkpOSktLufrqqxkcHOTcuXMEg0FOnjzJunXrUjbkKiLnTFFZWRk7duyQItd9+/Zd1padDmsg8R4S77k6nQ6TyURDQwPd3d04nU46Ozulxo+VhE7stlWFKUXo9foVV1+Mj4/jdrsZHByMuSNtJdKZyhO337pcLqanp2loaIh52WC8JLJePVZEQRIj3urqaoqKivB4PJw+fZqmpibZCr1KIXbtDQwMYDAYKCsrw+PxcOTIEelGkkk3g5UIhUJ4vV5cLheFhYXU1tbS1dXF6dOnuXTpEq2trdTV1a0aFwYxso1sy66vr8dqtaalxiR25CX68xNb5w0Gg+Qm4XA4pA7PpR5SRUHMpPdi5lyJDBQVFS1ZY4qst9TV1bFr1y7ZhtbSkcoT3b6PHj3K3Nyc1MaeyjdXPOvVYyUQCNDe3i5FSDU1NbS2tkoprsbGRkmciouLaWpqkr1VVm7E7az9/f3U19dzzTXXSKmi6upqqdbhdrtjfqpNJ+FwGJ/Ph9PpJCcnhw0bNkheew0NDYyOjnLx4kXOnDlDW1sbLS0t1NfXyypQqWxNF2uD4oPE/v37KSkpUXSgHOQzUo3sTBwcHMThcOB0OqV6Z3T2Id4UohJcccI0PT0t/bc4rCjaBhmNxpTUKZRM5Ykmnk6nk3A4TF1dXcLry+NFzohpbm5uQcouWpBERFfmhoYGaQ1CeXk5NpstZQa5iTI1NYXD4ZDWvFx99dWXPfyItY7y8nKGhoYkgZLL2UFOBEGQjEa1Wi2tra2Lto2XlpayZ88exsbGJKPVtrY2WltbZRMoQRBS/rMR2/9tNhvnzp1jYmKCU6dOKfZek9vAVWz8qKqqYnh4GIfDsaCuJr43M217LVxhwlRSUsLU1BTBYJCBgQG6urqYmZmhoaGBjRs3pqzzSYlUnjjoKzYDGAwG7Ha7LPYrsSJHxDQ3N0d7ezsDAwPA0oIUTXZ2Ns3NzTQ2NuJyuThy5Ii0ijtdxqoi09PTOBwOent7qa2tZc+ePSvWkEQLmoqKCskLz+VyJeyFJyeRhrHhcFjyoVvpxlVSUsKePXvw+/2cP39eEqiWlhYMBkNSN75wOKxY52JhYSH19fUIgkB2djYHDx5M2WBrJKl0Fi8vL6e8vHxB2rKurg6r1UowGMyoByK4woQpPz+fkZERtmzZwrve9S5uu+02RaKJVKbyoteXi+vYQ6EQdrtdcf+6RAVYFKT+/n4p1dDS0hJ39Bq5msLhcEjGqlarVfEFkTMzMzgcDnp6eqipqWH37t1xi2TkvE1/fz8Oh0MSqHg6q+Qg0p9vbm4u7u4ukeLi4gUCde7cOSmCSlSglO6SC4fD5OTkLFj7fuTIESlal3u/GijTcBGZtnQ6nbz66qsUFxen9UFoMa4IYZqenua73/0uX/ziF5mamuLBBx/kgx/8oGI3qlRETKFQiO7ubjwej5TOipxREAVJySJtIhHT3NwcbW1tDAwMoNFoqKurY82aNUnXwkRPN1GgDhw4IIstUCzMzMzgcrnw+XxUVVWxa9eupD3MNBoNNTU1VFdXS4Vrp9Mpu2v2UkTaIcnlzxcpUBcuXJAEqqWlRTI/jhUl7Y/E88Sf+VJr3202m6wNOUp+lvV6PRs3bqSpqYnz588TCAReP6m8Z555hscee4ze3l42b97M008/zVVXXSXrGcPDw6xfv57q6moeeOABHnzwQe69915FQ1Pxhi3HU10wGJQEabn15eI5SnYDxuM2kSpBikb0rRsfH18wM2Q2m2VP/czOzkpr2SsrK2URpGgiC9fiynRRoJayxEqGsbExOjs78fv9KbNDKi4uZvfu3ZJAnT9/nvb29rgESokVFJEs9lmOXPvudrsl5wWbzSbL4sV0LAnMz8+npqaGnp4eRc9diZQJ0w9/+EMeeOABvvOd77Br1y6eeOIJbr75Ztra2qiurpbtnPLycn7605+ye/duurq6+MQnPsHExISinVvimymZUDzSCik/P3/F9eXiXJGS3YCxzDFFC1J9fT2tra0pb0UtKiqSXBc6OzvZv3+/1Cab7Nlzc3O4XC68Xi8VFRVcddVVKS+Gi2K+lEAlexMUhXx4eHiBU34qEQVqfHyc8+fPSwLV3NxMQ0PDsq9J6VTeciIhPjCazWapY7SgoACbzRazK3u8Z6aSTFurDikUpm9961vceeed3H777QB85zvf4de//jX/7//9Px566CFZz9qzZw8wPzgH80+BSgpTMtFL5PrywsJC1q9fT1lZWcwrBzIlYpqbm+PSpUsMDg6i0WgwGAy0tLQoPhtRUlLC9u3bpdSUx+NJuCV7bm4Ot9uN1+ulrKyMnTt3JjyEnSiia3ZtbS3d3d10dnbidDoTXi0+NTWF3W6nv78fg8HANddco/jwclFREbt372ZiYoLz589L22ibmppobGxc9DWlM5W3FNnZ2dhsNkwmE16vl3PnzpGXl3dZ2j1W0ilMmTTDBCkSprm5OY4fP84nP/lJ6c+0Wi033XQTBw8eTMWRwHxYqtPpFN1iC/OvLd7oZW5uTtoWK64vF4U1nnOV3iobfV6mCFI05eXllJWVMTQ0RGdnJx6PJ+aOt0AggNvtxuPxSO4N6Z6ditw7JG5udTgcMT+lRzZqLNXKrjR6vZ5du3ZJAnXx4kU6Ojqk7svI15SOVF6sIpGVlSW5l3u9Xi5evLhgq26sApUuYco0nzxIkTANDg4SCoWoqalZ8Oc1NTVcunQpFUcC8x/e/Px8/H5/ys5YilgbIJZbXx4v6YiYxFTe7OwsbW1tkiAZjUaam5sz6g0e2ZIttj+73e4lbxjBYBCPx4Pb7aa4uJitW7fG/bCQarRaLQ0NDZJAXbx4URKoxRymZ2dncTqd+Hw+qqurE+ocTDVLCVRTUxMmk0l6IFI6lRdvalNc+97Y2EhXVxft7e10dnZis9liamBJhw2SeG4mfW7hCunKiyRdwrRSy/j09DQej4eenh4qKirYvn170nUKpa2QNBoNs7OznD59mqGhIekpPtMEKZrIjreenh5pZkhMh0Va7ej1ejZv3pzx9kfijjCDwUBXVxcXLlwgLy9PKsQHg0GpLlZeXq5IXSxZRIGanJzk/PnzXLp0ic7OTpqamtKSyks0etFqtQmtfQ+FQmnxhAwGgxlnJpySu0llZSU6ne4yp+++vj5qa2tTcSQwfwNK5Xr15VjK/SHSLHal9eXxomTENDMzg9/vlwSpoaGBpqamjBakaMRmjNraWinaaGtrIxQKUVhYyKZNm2JedZIpiAaeRqNRWoGg0+kIBoNSvS3dach4KSws5KqrrlogUAADAwMJr3WIFzkitOi17w6HY9m172rE9BdScjU5OTls376dl19+mb/7u78D5n/RL7/8Mvfcc08qjpTIz89PmzBFikQi68vjRQlhmpmZ4dKlSwwPD6PRaNBqtRQWFlJTU5Nxb+ZYEVv7o2fC0rGxVE7E6xe7J8PhsOIejnKSl5dHTU2NtPzT5/PR19eH1WrFYrGk9HclZ+ow1rXv6TCOhdeRMAE88MADvO9972PHjh1cddVVPPHEE0xOTkpdeqmisLAwLcIkpvIi15fX1tbKaha72JmpEqZIQRJTEzabDQCPx8OpU6coKSmhubk541NEIuFwmK6uLpxOp7SRtbKyklAohNvtXlVGsSLhcJju7m4cDofkVFBZWSnVy8TXlCq3glQgup04HA6ys7PZtGkTFRUVTE9PSy3mdrsdm82G2WxOSZSRiugleu17pECZTCa1+SGClF3NO97xDgYGBnjkkUfo7e1ly5Yt/Pa3v72sIUJuCgoKkl6vngiCIOB2u5mamqK+vj6h9eXxkgphmpmZ4eLFi4yMjCwQpMg3rtVqxWg0SjYt1dXV2Gy2jN0xFOmOnZ2dzdq1axd0smVlZWGz2WhoaMDtdnPs2DEqKipoamqSfYBWLgRBoLe3F7vdvqjBqtjK3NjYKA2Dim4FmSq6giAwMDBAZ2cn4XCYpqamBTNbBQUF7Ny5k6mpqQUCZbVaMZvNst7UUxm9RNpQiUa+TqeTrKwsWWc8YyUTIyaNoLS3e4r5q7/6K5qbm/nKV76iyHniOo2hoSHKyspYt26dYgXMM2fOUF5ejtFoTPp7RQuS0Wi8TJAWI9LAVClLoFiJfPLW6XQxz/5Ed7JlkuhGGqyGQiFsNltMjhDRM1k2m03xmazlGB4eprOzk+np6RWbBETECGpkZASdTierQB04cEBaVa4Ew8PDHD9+HEEQpLXvSnyOBEHglVdeYdOmTYq91ljILJmUAb1en/KISRAERkdHpfXloq1KSUmJojdlOSKmaEEymUxYrdaYn6Dy8/NZv349JpMJu93Oq6++Ku19UcoNOppwOCwVmzUazWVP3iuRm5vLmjVrJB++gwcPSk7MqY6Cl0JcCNnZ2cns7GzMN28R0a3AZDJJfm8VFRVpXx/i9/vp7OxkbGxMarWOVVjy8/PZsWOHJFDilmA5BErp9vTy8nLy8vJoaGhgaGhowVbdVL/nXlepvHRRWFi45LLAZBH3O7lcLiYnJ2loaJDWl1+8eFHxQnMyrubT09NcunRJEiSz2YzFYkn4DSq2WY+OjtLZ2UlXV5fiS/DE9JbD4UAQhKT95UTRNZvNkuiKNwsl22tHRkbo7OxkcnIy6Z9pTk4OLS0tkkAdOXKEyspKbDabomnLqakpOjs7GRgYkD5Hif5MIwVKdJFwOBxYLBYsFktCP6t0rVYvKSnBbDYvu/Y9FeeqwpRi9Ho9g4ODsn5PcR2Ay+WS9jtFry9P53r1eJiamuLSpUuMjo7KIkjRiE4JkY4LsQ4YJoq4PNHhcBAKhWR35BZbycfHxyUfPiWiQjGaGB0dld1gNXJ9iMvl4vDhw4rst5qdncXhcNDd3U1tbe2C7b7Jkp+fz/bt2yWBEu2bzGYzVqs1LoFK12p18T0rrn0XPQ0j177LmVYWBEEVJiVYbr16vIjFWJfLJa0vX2rLaDrWq8cjTNGCZLFYMJvNKXlDRjou9PX10dnZidvtpqmpKSmTy2jEeovD4SAQCCS8PyhWioqK2Lp1K6Ojo5KTuVxGsZFMTExgt9sZHByksbExqWhiJfLy8qS0pdPp5NChQ9TU1Mh+AxRtntxuN5WVlSl1oIgWKHGg2mQyYbPZYhIcpVN54pnR1yaaE0eufRf3j8kR4Yr3LFWYUkz0evVECIfD9Pf343K5CIVCmEwm6urqln1D63Q6AoFAUufGSyxiGClIOp0upYIUjdgeW11dLQ20ulwumpqaknJWiFxol0i9JVnEqFAs2Is+fMnuMIpczW4wGHjDG96gWM0yPz+fdevWYbFYpLqaHAsYRVcNp9NJUVERO3bsUKwrMFKgROsml8uF2WxeUaCUFiYxclnqmsS1701NTdL+MbExJ5kaoXj/eN24i6eLZCImsWjudrsRBAGz2RxzjWIp54dUotVqlxTDqakpLl68yNjYmNSxZDKZ0vJkFOnvJs7WlJSU0NTUFFdnmNgAYLfbmZmZwWw2y7LQLlHKy8vZuXMng4ODUlSYSNQWabBaW1ubVoNVsa4mCtSBAweoq6uLu8YRPV+VTleN/Px8tm3bxuzsLOfPn19RoESRUNo0FlYWiIKCAjZs2CBt1T148KBUI0xE8IPBIFqtVhWmVJNIxBS5vlysu9TU1MT1xsyUVF4mCVI0YsRmMBikzrCqqiqampqWTRuJTSd2u52pqSnFmyqWQ6PRUFVVRWVlpTTVL/rwrdQJODc3h9PppKuri6qqqowyWBVvgJECVV9fv2KXmJhe7ezsRBAEWlpaFLMRWonc3NxFBcpkMtHU1CS9n8QJGiXfX/FGLmKEK27VPXLkiDQGEI/xcDAYzIjPUTTpv1vJTElJCVNTUwiCsOKHQVxf7na7pYHERHbcQPqbHyYnJ7l48SJ+v1+a2TGZTBn5phM7wxobG7Hb7Rw8eFAq7EanriJXfptMpgUWLplEZNqyp6dHKrwvVleLXKtRVlaW0Qar4oZgscbx6quvLjmvNjw8TEdHBzMzM9hstpTW+5IhUqAuXLiA0+nE7XZLAiUKk9Ju5omcKdYIrVYrTqeTY8eOUVJSIq19X+lelomt4nAFClNxcfGKEVMwGMTn8+HxeMjLy6O1tXXRlQHxkK6IaXZ2lmPHji0QpFTZtMhNXl6e1I4d2e1mNpulBoDx8fGUrfxOBVqtFoPBQF1dnVRXExf7lZaWSi7mxcXFbNu2bdXYBIk1jvHxcakI39DQgNlsZmZmho6ODvx+f9yzSOkkNzeXrVu3XiZQ4sB6OjbmJnoPysnJWbD2/eTJkzGtfU/23FSR+Z/0OCkpKVlSmAKBgLS+vKCggLVr1y77S4sHpSOm8fFxPB4PMzMz6HQ6aXfNahCkaAoLC9m8eTNjY2NcvHgRt9uNRqOhsbGRzZs3p21QNxki62qiD58gCBQUFEjeb6uRoqIiNm/ejN/vp729nX379gFgNBoVWc+eCiIF6uLFi3g8HgDa29tpaWlRRGTl8smLd+17JraKwxUqTHNzc8zNzUmphsj15Xq9ng0bNsS8vjxWlGp+GB8fp62tTYqQSkpKGB8fZ3p6mkAgkDF2QPEyNjaGw+FgamqK6upqJicn6enpIT8/P2NTQish1i59Ph/5+fno9XoGBgak1HEmWQLFw8zMDF1dXYyNjVFZWUkgEKC7u5usrKy0On4ky8TEBDMzM+Tk5JCXlydZODU0NKRcoORutlhs7Xtubq5ksyTe+9Qak0KIqRG/309JSYm0LbakpIRNmzalLHWS6lTe+Pg4ly5dYnx8nKysrAUR0uTkJJ2dnZIdkFLt4HIgDhAODQ3R0NDA+vXrycnJkYZmI2egEq3/KU2kwapGo6G5uVm6GczNzUmNH+lwXEiGQCCAy+XC4/Fc1qwROdvV2NhIY2PjqhGo8fFxOjo6GBsbw2KxSI01s7OzXLp0SRIoo9FIa2trSm7kqXIWj1z73tXVJS1fFF1RMjViuuJMXMVI6ZZbbqG8vJw777wTk8mU8tmJiYkJTpw4wXXXXSfr940WJPFNttjT1ejoKB0dHUxOTmKz2RSd7YmXyCFSo9G4pGml2HZst9vJy8ujqakpY9Ng4kC23W4nGAwu60AxMzOD0+mku7s7JQOtchIKhfB4PFJtbLm1ICMjIwtqg3IPH8vJzMwMnZ2d9PX1LWs1NTc3x8WLF+nv70ej0dDQ0CC7QIkdnVdffbVs33MxRKd90UeysLCQ7Oxsdu7cmdJz4+WKEian08nXvvY1/vVf/5WdO3fypS99iT179ihy9tTUFIcPH+aNb3yjLN/P7/fT1tYmCZLVaqWhoWFFoYlcHSAIQsZFGmJ318DAAAaDAbPZHJMlTfTNsbm5OWNSYWI7e2dnJzMzM9KwbSwPBZHu7Ok2io0mehapubk5pocCQRAkf79Ma++HhZFfdXU1TU1NMc1oBQIBLly4kBKB6u7uxuv1smvXrqS/VyyIaea2tjaqqqrYsWOHIufGyhUjTHfffTfPPfcct956K7/85S/5/ve/zw033KDY+bOzs7z66qvccMMNSUUpfr+fS5cuMTExEZcgRRMZaeTn59Pc3BzXfIPcTE5O4nA46O/vj2keZimi00mp9nZbCdG0dnx8PKmOtMnJSex2OwMDA2kxio0kchYJSPjhJlKwp6enZXHHSIZwOLzAhSLRh5tAIMDFixfp6+tDo9FgNBpZs2ZNUq/L6/XS19enuECcO3eOnJwcNm7cqOi5K5FyYfryl7/Mr3/9a06dOkVOTg6jo6OXfY3H4+Guu+7iT3/6E3q9nve973189atfjSsF8IMf/IBt27bR3NyMwWDgG9/4BrfccouMr2R5gsEg+/bt47rrrksodREtSDabLean7uUQt7O6XC7Ky8sVX343NTWF0+mUIgK5XJIj3RLSEWn4/X7sdjsjIyNSykqOmkrk903H+pChoSE6OjqYm5uTzXsw2kJKHLJW2nW+s7OTrKwsKfJLNosQCAS4dOkSvb29SQuUy+ViZGSErVu3JnVN8XL69GmKiopYu3atoueuRMqTv3Nzc/zDP/wDe/bs4bnnnrvs70OhEG9729uora3lwIED9PT0cNttt5GdnR3Xsr93vvOd0v8uKChQfL26+OGNt5g4NjZGW1sbExMTZGdn09raKosgiYjuDwaDAYfDweHDh6mrq8Nms6W0g296ehqn00lPTw81NTXs2bNH1hpKXl4e69atu2wPlNlsTumNPLI2FtmsIRfFxcWSUWxnZyder1eR2aCxsTE6Ozvx+/0LGgDkINIdQ6zBuVwuSaBSWQcdHh6mvb2dubk5mpqaqKurky2tnZ2dzcaNG1mzZg2XLl2iq6uLrq4uDAYDa9eujevnl6616q/75ofnn3+e+++//7KI6X/+53/467/+a6kIDPCd73yHBx98kIGBgYQ+9Bs3buR973sfH/zgB+W49Jj505/+xK5du2K6AY+NjXHp0iUmJyfJzs6W1pWnullB7OAbGhrCZDLJblcUWdSvrq7GarUqkmqLvLGm4kY+PT2N3W6nr68vqVRkPCRTu4oV8f0gOpmnWtjh8i28FotF9pGAyE47pYZ+A4EAbW1t9Pb2AlBfX8+aNWti+nmK4rlhw4aUXmM0R44ckVLHmUTapfLgwYNs3LhxwVrfm2++mbvuuovz588nFNoWFhamfIvtYsQyZDs6OkpbW5skSGvWrFG0e04cZhU7+Lq6umRx545cR15VVcWuXbsUTRmWlJQsugcq2RtedPec3JHfcmg0GioqKigvL5ciDbfbLcuNPDIVWl9fL+tepJXQaDTU1NRQXV29wF8w2cWOMP+67HY7vb29GI3GlK4MiSY7O5sNGzbQ2tpKW1sb3d3ddHd3xyRQ6YyYMqUpJZK0C1Nvb+9lu+bF/xafPOKloKAgLcK03CxTtCCtXbs2rYOjpaWl7Nixg4GBATo6OvB4PAkVuWdnZ3G5XHR1dVFZWZl23zfxRi4W791u92VDhbEgzht5vV4qKysVF9pINBoN1dXVVFVVSRt6YzWKjSYQCOB0OvF6vWk3jhX9BWtqaqS5L4fDkfDriuy0S6dDeyICFQ6H0zL3FQwGM3LeLCFheuihh/j617++7NdcvHiRNWvWJHRRyZLOiClamEZHR7l06RJTU1Pk5OSkXZAiEW94lZWVdHd3S8OEsXTwzc3NSUakFRUV7Ny5M2Pat8Un8qqqKrq7u2lvb8flcsXU7hwIBPB4PLjdbsrKyjLuddXV1VFTUyMZxYoCtdICxsh2+5KSkox/XU6nE6vVuuIDRWSnnV6vz6jXFSlQ7e3tkkDV1dWxdu3aBYIQCoUUHxNYaQdUOklImD760Y/y/ve/f9mvsVqtMX2v2tpajhw5suDP+vr6pL9LhExI5UUL0rp161K6XjwZtFotRqOR2tpayQCyvLyc5ubmy56mI52xxahLqcVv8SK+rrq6OrxeL2fOnFlyQDTyxl1UVJTRBquRRrFdXV2SUay4gDHyRh45UJmXl8fmzZuTWtKYSiJfV3d3Nx0dHVIEFR3JR3fabdiwQTbfS7nJzs5m/fr1tLS00N7eTk9Pj9RNKgqU2vywkISuqKqqiqqqKlkuYM+ePXz5y1+mv7+f6upqAH7/+99TXFzMunXrEvqeer2e4eFhWa4vHrRaLX6/XxoszMnJYf369UnnzZUisk3d4XBw6NAhqYNPq9Xi8XjweDyrzhlbp9NhNpulPVDHjh2jsrJSGqzs6urC6XSSl5eX1oV28aLVamlsbMRgMODxeDhz5gxFRUWS8IqWThqNhjVr1mTUoPVyiA8U9fX1+Hw+2traJIGqqqpiZGQkZZ12qWQpgaqtrSUQCKRFmK6oVF48eDwehoeH8Xg8hEIhTp06BSDN07zlLW9h3bp1vPe97+XRRx+lt7eXhx9+mLvvvjvhduaioiLJIVgphoeHmZiYwO/3rzpBiiY3N5e1a9fS2NhIe3s7r7zyChqNhuLiYrZs2ZLWQd1kyM7Oprm5mYaGBml9uFarlV7vSumwTEVcwGg0GnG5XBw/fhyNRiO5zmdqpL4SkQ7tPp+P8+fPIwgCgiBgtVpXzXqNaESBWrNmjVSDEtNqtbW1iglFOBxGEIQrJ2KKh0ceeYTvfe970n+LXXZ/+tOfuOGGG9DpdPzqV7/irrvuYs+ePRQWFvK+972PL3zhCwmfWVhYGPcW20QZHh6mra2N6elpsrOz0el0ZGdnk5OTsypvBiLBYJD+/n7GxsYoLCxEEASmpqaYmJigpKRk1b42QRAYHR1lZGSE3NxccnJyGB8fZ3R0lLKysox8eoyVqakpxsbG0Gq16PV6xsbGGBoaorS0NGM24yZCIBBgfHyccDhMcXExExMTDAwMUFxcvGqi26XIy8tDo9GQl5eH3+/n5Zdfpq6ujnXr1qX8vRgMBgFlN/XGyhVjSRTJo48+yg9+8AP27t2bku8veoGJgpSbm0tLS4uUivR6vTgcDkpKSmhpaVk17tEwn3MWl9mJe1zEmoTYwQeJ29SkC9F9oLOzk0AgsMDVQEy/KjnzIifi0O/Q0NACt4joVnebzZa2TrVEiLafampqoqCgQKoHut1uCgsLF7xHVwOCIEhNHuK9o6ysjFAoRHt7Oz6fD0EQqK2tZf369SkTqKmpKfbt28ctt9yScQ+aV6QwPfvsszz55JMcPXpU1u8bDoeltu9oQYr+xc7NzeF0Ounq6lLEaSFZQqEQPp9PqrUstfky2oOvpaUl42tN0X5tS9nhRK4Gl2O2K9VEzuwstZoe5m9ADodD0eHgZAiHw3R1deFwONDr9TQ3Ny/aYBMMBiWBEre1ZnqaWXSiCAQCNDU1LdoWHwqFpBnDVArU+Pg4hw4d4m1ve1vGPWBekcL0/e9/n09+8pOcP39elu8XDoelgutKghRNpNOC2WzGZDJl1NO42LXldDql5WKx1FqCwSButxu3271kB1+6EXcEiW4QDQ0NK+bTI10JwuEwTU1Ncc9ApZrIGavq6mpsNltMQ7/RdkpmszltRrGLEbmDS6vV0tzcTGVl5Yo/e7HFX2zMEdfYZxKTk5N0dHQwPDwsra5Z6T4QLVA1NTVs2LBBNoEaHh7m9OnT3HzzzRn1/oYrVJh+9atfcfvtt0uL2hIlHA5LTzgzMzPk5ubS2tpKVVVV3E/SYuovUzqJxMjH6XRKLuaJpOZmZ2dxOBzSAOFST+1KMj4+TmdnZ1JGqOJaALvdTk5OjrQHKp2/M9GQ1+12U1JSQnNzc0LDzGLqcnR0NCW2VIkgRquzs7OSY0e8P+voUQabzZb2UYa5uTkcDgc+n4/6+npsNlvcDwOLCdS6deuS/pwNDAxw8eJFbrrpJlWYlGDv3r287W1vo7u7O6EfeLQg5eXl0dLSkpAgRSLOXnR0dJCTk0Nra6viqQfxhutwOCSDVzkiglR78MV6DXKvjoisuen1epqamhR/Go+cRcrPz5fmlZJF3Jk0OTmZtp1JExMTdHR0MDo6Klt9Txz+9nq9lJWVYbPZFB+6DYfDeDwenE4npaWlNDc3J11rDoVCdHZ20tXVRTgcprq6mvXr1ycsUKKLiFw75OTkihSm06dPs23bNoaGhuJ6ky8mSK2trVRWVspaaxCLt06nU7E0mFhwFTdXir5kcj8pjY6OSilPpeo0Sizbi3war6ioUGQleuSKdq1WS1NTk+wt7UoYxS5GtKddKvZPRdZ5KysrsVqtKbfLikxH6nQ6WlpaZN+4LJdAdXV14fP5ZN+6LQdXpDCJlvo+ny+mN2I4HJb20IiCtGbNGioqKlL6AZ2dncVut9PT04PBYMBqtcr+4RRvbg6HQ5r/SPV8lbhFV+zga25uTsmMUKRxrFLrySNTl7W1tdhsNtlFUBAE6f0YDAax2WwpT/1Gbj4OhULLroVPhqU67VJJtMGw1WpNyUNF5ENZU1NTQunIeAiFQtjtdrxeL+FwmKqqKjZs2BCzQLndbgYHB1O+zj0RrkhhGhsbo7S0lAsXLmA0Gpf8OlGQ2tvbmZ2dVUyQopmYmKC9vZ2xsbGEN9ZGo8RqgZWITkHJ1cEX6dNXWVmpSPQSzdTUFHa7nf7+flmf+EXX98nJybRsfI2M0jQaTUIGuIsRa6ddKknVSpbp6Wk6OjoYGBiQGpyUTGMnKlB2u52JiQmuuuoqha40dq5IYQoGg+Tm5nLw4MFFNzOGw2EGBwelYmt+fj5r1qyhvLw8re3BokiGQiGam5sTXmctrkgIBAKKLGNbicgOvoqKCpqamhK6IYjfx+PxUFJSkhHF7ci9P+Im20RuShMTE3R2djI8PJyWzbXRiM0xDoeD7OxsmpqaYuqQiybRTrtUEr3EMtFIO9KpPVXRczyEQiEcDgcej4dwOExlZSUbNmxY8ppEW6ft27crfKUrc0UKkyAIFBUV8bOf/Yzdu3dLf56pghSJIAj4fD7sdjsFBQW0tLTEdPNN9/rqWEi0gy+yAaGwsJCmpqaMm1eJnJWKp7YWWR8zGAxYLJa0dzVGEgqFJC/BgoKCuBov5Oi0SyVTU1M4nU56e3upra3FarXGNIAcGf0VFRXR0tKS1lUv0YRCIZxOJ263e1mBunDhAlqtls2bN6fpSpfmihWmqqoq/vVf/5U3v/nNhMNhKX+eqYIUTTAYxOVy4Xa7qa6ulgxHoxHrEXa7nZmZGcxms+Lpn3iJtYMvMhWYm5ubES3byxFZpwmHw8vuFYoszMczi5QugsGg9HCw0qxQKjrtUsnk5CROp5O+vr5lm2fEh7/29nYAWlpa0h79LcdKAnX27Fny8/NZv359mq/0cq5YYTKZTHz+859ny5Yt9Pb2Mjc3R0FBgVRDWi3MzMzQ2dlJX1/fgtXXYjeV3W5namoqbe2+ybBUB184HJbqHDqdbtG1B5mM2AFpt9vJyspakAaLTGuWlZXR1NSUUU/bKxHZnRh9/ZGddqlq5kklExMTOBwOBgYGMBgMmM1m6Sbu9/tpb29nYmICm82W9vR4PIRCIekhNxQKUVFRQXNzMy6Xi9LSUlpbW9N9iZdxRQoTzK/mmJ2dJRQK8fOf/5y1a9euKj+taCI/GHV1dfj9fiYmJmJ2NMhUxCaNzs5OBEGgurqagYEBBEFIaJNpJhGdBispKaG3t5f8/PyYljFmMpERX0VFBTk5OfT09CjWaZdKIh0yamtrCYVCDAwMLHgwXI2IAnXw4EE+8YlPcP311/ORj3yEG264Id2XdhlXnDD95Cc/4b777sPn86HRaLjzzjt57LHHVu2NO5KRkREuXbrExMQE2dnZrFmzJuPschJBTIFdunRJ6o5cv379qn6QEBFrhmLrt7hnbDVFSUsRDodxOp24XC4pVbRmzZpVZRS7FMFgUDJUBTAYDDQ1Na2qCHApvF4vt99+O4cOHaKgoIBPfepT3HPPPRmz+Rcg5bGoy+XijjvuwGKxkJ+fj81m47Of/Sxzc3MLvu7MmTNce+215OXl0dDQwKOPPhr3WTfeeCN///d/T3l5OQcPHuSJJ57gpz/9Kbt37+YPf/gDq1WDx8bGOHHiBCdPnqSqqorrrrsOq9XKpUuXOHHiBOPj4+m+xIQZHh7m6NGjXLhwAZPJxHXXXUd9fT2nTp3i9OnTTE5OpvsSE0IU20OHDuF0OmltbeXaa6+lpKSEI0eOcO7cOcVWs8iN2FJ+4MAB+vr62LRpE1dffTVZWVkcOHBAesBYjYgPEq+++ioTExPs3LmTXbt2MTs7y/79++no6Ljs3rVamJmZ4Ytf/CLbt29Ho9Fw+PBhfvazn/Gb3/wGs9nMuXPn0n2JEimPmH7729/ywx/+kHe96100NTVx7tw57rzzTt773vfyjW98A5hPU7W0tHDTTTfxyU9+krNnz/KBD3yAJ554gn/6p3+K+axf/epX1NTUsHPnTunPRkdH+fSnP81zzz3HDTfcwKOPPkpTU5PsrzMV+P1+7HY7IyMjNDQ0YDKZFjyxZVq7ajyMjY3R2dmJ3+9ftM06Ez34YiVyFslqtV7mpBDp9p0q14NUsVKnndj2HrmCY7W8tpXGNcbGxrDb7YyOjmZES3+sCILAj370Iz772c+i0Wj46le/yrve9S7ptQmCwN69e3nDG96QMZmltKTyHnvsMZ599lkcDgcwv6bi05/+NL29vdKb+KGHHuLnP/85ly5dkuXMS5cucf/99/PnP/+ZD3zgAzzyyCMZFbpGMj4+Lu3XicUFempqis7OTgYHBzGZTJjN5oxtghBf2/DwsPTalvtwR7oyZ4rh6FJEmsfGcq3xfn06ETvtRkZGYnLHFm/iY2Nj0k0801/b6Oio9NqWa2wYGRnB4XDg9/tpbGyksbExIwVKEAROnjzJxz72Mc6fP8/999/Ppz/96VXx8JoWYXr44Yf57W9/y7FjxwC47bbb8Pv9/PznP5e+5k9/+hNvetObGB4elq1ILAgC//M//8MDDzzA8PAwn/zkJ/m///f/ZsxNPLLoajQaMZvNcUUJYpfbzMxMxs2NTE5O4nA46O/vT2heJ7KDT3xtmdIVNT09jd1uTzgCGhkZoaOjg6mpqUUjrHSSbKed2DmaTqPYpZibm8Nut9Pd3Z3Ua8vEJqT+/n4+9alP8ZOf/IS3v/3tfOMb38BgMKT7smJGcWHq7Oxk+/btfOMb3+DOO+8E4C1veQsWi4V/+Zd/kb7uwoULrF+/ngsXLizq3pAMwWCQJ598kq985SvU1dXx6KOPcv3116ftJh59045sU40XcdK+o6OD7OxsWlpa0tpEEG2wKtYaEyGyg0+j0aTE1DQeIlcaJLshNnLDrlL+eMshtoW73e6kO+3EWTtxjjDdSxjlNFHOtLGN2dlZvvWtb/Hkk0+ydu1annzySXbt2pUxD6ixkrAwPfTQQ3z9619f9msuXrzImjVrpP/2+Xxcf/313HDDDfz7v/+79OdKC5OIGDU9//zz3HTTTTz66KNYLJaUnLUYkbWGZG/a0YhuCaLtfktLi6KL/CKNM+X0JYOFg7cFBQU0NzcruooilbNIy81AKUGkq0FhYWHMziOxEL2EUTSKVeq1iU0bnZ2dsj+0pXvQXRAEfvazn/Hwww8TCoX48pe/zLvf/e6MiU7jJWFhGhgYYGhoaNmviQyNu7u7ueGGG9i9ezfPP//8gqclpVJ5S3H+/Hnuu+8+Dhw4wB133MHDDz+c0nbeyCgiHiuURJBjUVk8iO7RXq+X8vJybDZbyn6WcnnwxUr0TTuVghh9VqptmJT0tItcwaLVahUZoBY3UM/OzqZ0Uedi1mCpTs2eOXOGj33sY5w6dYp7772XRx55ZNW37CuSyvP5fLzxjW9k+/bt/Od//udlKi42P/T19UlFxE996lP89Kc/la35YSUEQeCXv/wlH/vYxxgbG+Phhx/m/e9/v6xPHHKZRyZCdIFX7nRDMBjE4/HgdrspLi6mqalJMYPVyPUhqejgS2cUo4RThNhpF1mbVCLNJka+TqeTnJwcbDab7D/XqakpOjo6GBoakpy/lYgilDBTHhgY4OGHH+ZHP/oRf/M3f8M3v/lNGhsbZfv+6STlwuTz+bjhhhswmUx873vfW/CmqK2tBeY7eFpbW3nLW97Cgw8+yLlz5/jABz7A448/Hle7uBwEg0G++c1v8vWvf52GhgYee+wxrrnmmqQ+LKmy208EcRFiIBCgubk56QHdZEw+5SbSoVuOLrfoHUVNTU1pc6KIdFpItp4lEtlpp+RNO5rI91BhYSE2my3p91AgEMDhcNDV1UVdXR02my0t4wbR62fk2HMVCAR44oknePzxx2lqauKJJ55I+h6VaaRcmJ5//nluv/32Rf8u8ugzZ85w9913c/ToUSorK7n33nt58MEHU3lpyzI0NMQnPvEJ/vM//5Obb76Zr3/965hMpri+x+zsLC6Xi66urpQuKIsXMQLo6OhIeE9S5FqEVD3tJorY5ZZMB1+mdspFdgAm6kc3MzODw+FI6YLKRJAj6g6Hw3i9XhwOByUlJTQ3N2eEy4YcCzvFrM6nP/1ppqen+eIXvyh7VidTuOIsieTmzJkz3HfffRw+fJgPfvCDfPrTn14x/TY3NyfVWcQ13Jnw4Ygm0tyxsrIyps6rdNQHEiHRDr7VMluUSHQY6VifyZ52kVtuY/38iNFte3s7Wq1Wcv7ONESDYofDgUajkQRqpffluXPn+PjHP86xY8f40Ic+xOc+97m0ZV2UQBWmGBA7Xj7xiU8wNTXFZz7zGd7znvdc9qQiblb1er2UlZVhs9kydog3kshZlYaGBiwWy2UDg9EpiXS3M8eKWMew2+3LNiysVjeG6I23i20/TmWnXSqJ7uxcajXI2NgY7e3tTE1NZdyM21KEw2HpAU+n02G1WhdNqw8NDfHII4/w4osv8ra3vY1vfetbmM3m9Fy0gqjCFAeBQIBHH32Uxx57DKvVymOPPcbu3bsZGBiQbFpKS0szYrNqIoyPj9Pe3s74+LiUvtJoNBk1g5IoYrQgPoWLHXyRN79Ud0imiqVmoGB+0LKjoyNjtscmQvQsnLgvaXp6ms7OTvr7+yXHk0yMbpdDTIk7nU6ysrIIBAJcc801hMNhvv3tb/PNb34Tk8nE448/ntZZS6VRhSkB+vv7+fjHP85//dd/YbFY8Hq9vOtd7+KLX/yiovM0qUC8yYlu2FlZWczNzaV9cFAuxA6+7u5uioqKmJiYkIQqE+p/yRA5pwOg1WoloVoNUcRKiIPofX196PV6JicnZWsESTfhcBi3281b3vIWYP53GQqF+PznP8+dd9656j938bK636lpQmwYyMvLo7e3F0EQKC8vXzUmo8uh0WjIyckhNzeXQCDA9PQ0hYWFlJeXXxEfjqysLAoLC9HpdJK7d3Fx8aq/scH8766oqAi9Xs/c3Jy0QqSwsHDVixLMf+7KysrIyspiZmYGgNzc3FUXJS2GVqtlZmaGpqYmBgYGGBsbw2g0YjKZrojfXbyoEVMC7Ny5k5ycHL74xS9yww038NJLL/HQQw8RCAT43Oc+xzve8Y5VeROP9OoTjTc1Go2UAqupqaGpqWlVmEBGEz2L1NzcTEVFhVSjyUQPvnhYrNNOq9Xi8XhwuVyrcltuJOJK83A4TEtLC1VVVfj9/mUd6lcLIyMjfO5zn+OFF17g5ptv5vHHH6e2tpZnn32Wr3/963z+85/nQx/6ULovU1FUYUqAnp6eyzppZmdn+drXvsa3vvUtWlpa+MY3vsGOHTtWRU54amoKu92+rMHqas3nR84ihcPhRbfiio0dYi0m3R588RDdaWez2S7r1oqcgVquiSATGR8fp6Ojg7GxMWw226Jt+8PDw3R2djI1NSU5LayGB8NgMMizzz7Lo48+Sn19PU888QRvetObLlsjIgjCqn2gSBRVmGSmt7eXj33sY7z00kv87d/+LV/96lelQnSmEfmUHWvhP7oDymAwZOwNXLxhTU9Px9S0Ed3Bl8nda4nYI0U2ESTi8K4kkW4eRqMRq9W67GqJSCugubk5rFZrxka/giDw+9//noceeoiRkRE+85nPcNddd60KMVUKVZhSgCAIHDt2jPv+/+3dd1jTV/s/8HdAkI0CIlBWhEStMgQUGQ4qFa21to+1tfoVaq3WWYaCoILoI4rgwFVHvyo8to7q82htq7Q+1s0WcDCTsJQlwzCiBAif3x/+km9QVEYmntd1cV01SckJyuf+nHPuc9/+/rh//z5WrVqF4OBghVkCE7+Dft1d9puIzzBUVVXBZDJhaGgoxRH3jPhddm+WeMRnIcLzXYpyZkQSszvxM1CK1vBOIBCgtLQUJSUlMDQ0BIPB6NHs7uVCsV3NkOWJxWIhODgYt27dwrfffouoqCilOFIiayQwSRFFUTh58iTCwsIAAJs3b8acOXPk3sqgrKxMInsOL5+yZzKZcs1sE1+S7E6DxbdRtAoJkqhoIe7lM1DyXAIT7gGy2WwMHDgQTCazT0VrhQdZORwOVFVV5X4QvLGxEZGRkYiPj8cHH3yAuLg4MJlMuYxFGSjePPf/KykpwaJFi0StIGxsbLBx40a0trZ2et39+/cxYcIEaGhowMLCAjExMXIa8atoNBrmz5+PwsJC+Pr6YtWqVZgyZQoyMzMhy/uB9vZ2FBcX4/bt22hoaICTkxPGjBnT53VrFRUVWFlZwcPDA9ra2khNTUVubi74fL6ERt49fD4feXl5SE5OhoqKCtzd3cFkMvscRDQ0NPD+++/D1dUVLS0tuH37NoqKiiAQCCQ08u5pbm5GdnY2srKyYGRkBE9PT4mUSBo0aBBcXFwwevRoVFRUICkpCeXl5ejo6JDQyLunvr4eqamp4HA4YDAYGDduXJ8rqauoqMDMzAweHh6wsLBAfn4+UlNTUVtbK9PfPYFAgIMHD8LOzg63bt3ChQsX8McffyhtULK2tgaNRuv0FR0d3ek1krgmK+yMKTExEWfOnMFXX30FW1tbPHz4EIsXL8aCBQuwY8cOAC/uQphMJry9vREWFoYHDx7gm2++QVxcnMyLv3ZHeXk5Vq9ejfPnz2P27NnYunUrjI2NpfZ+wuKYJSUl0NTUlHqBVR6PBzabjbq6um613+4r8RmgsHSNNGdswtYJsqrCLcsZm/AMFIfDkVkCCI/HA4vFQn19vdT/vQj7k5WUlMisjci1a9ewdu1aVFdXY8OGDVi5cqVSJAy9ibW1NRYtWiRq8goAurq6oqVuSV2TFTYwdSU2NhYHDx5EUVERgP9rl1FVVSX6hQ0NDcWFCxdk1i6jpyiKQkpKCvz9/ZGfnw9/f38EBQVJdBNavMCqmpqazJvNPX36FAUFBWhra5NKRW7xi4yOjg4YDIbMkhRe3uORRjWF11WpkAXxJozSupmRdY8wceJtRKRVpYXD4SAkJATXrl3DwoULsXXrVqn3lJMVa2trBAQEICAgoMvnJXVNVqrAtGHDBiQmJiIjIwOA/BsM9oVAIMBPP/2E9evXQ01NDf/85z/x2Wef9ekCJ37XS6PRYGNj0+e2Fn0dC4vFksieAfB/9cU4HA7U1dVha2sLQ0NDuXw+adSf625dP1kQJiGUlpZCX18ftra2fd6k7+joELU0HzRoEBgMhtz2JMULLRsZGUlktt3U1ITNmzfj2LFjmDhxIuLi4qTWfVterK2t0dLSgra2NlhaWmLevHkIDAwUzQQldU1Wmnklm83Gvn37RMt4wIvU7JdboQ8dOlT0nCIHJlVVVfj5+eGLL77Apk2bsHTpUhw6dAg7duyAvb19j77Xy32DJNHzpa9oNBpMTU1hbGyMsrIyZGVl9fru/+VMKyaTKbeAK6SiogJLS0uYmZmhpKQEGRkZva7Y/XIl9Pfff1/u56iEhUXNzc1RUlKC9PT0Pn8+YRanvb293LM41dXVwWQyYWVlheLiYqSmpva6eadAIMCxY8dEM6Nz587ho48+UphMQEn6/vvv4eTkBAMDAyQlJSEsLAyVlZXYtWsXAMldk2V+5QoNDX1l8+zlr5enfOXl5Zg2bRrmzJnTaW2zP9DU1ER0dDRyc3NhYmKCSZMm4bvvvkNNTc1b/1+KolBXV4e0tDTk5eXB3NwcHh4eClVkVVVVFXQ6HR4eHlBTU0NKSopoma876uvrkZaWhvz8fFhYWMDd3V2h0n+FHW09PDwwYMAAJCcnIz8//5Ukndd5+vQp0tPTkZ+fDysrK4wfP16h2ogIL+AeHh5QVVVFcnIy8vLyup3gwuVyRZ+PTqdj/Pjxcg9K4gYOHIgRI0bA3d0dNBoNycnJyM3NFZU8ehOKonDz5k14eHhg8+bNWLNmDR4+fIgZM2YozN9fd/TkmhwUFITJkyfD3t4eS5cuxc6dO7Fv3z6JJzzJfCmvpqYGdXV1b3yN+CZvRUUFJk+ejPHjxyM+Pr7TBVeZl/K6QlEUbt++jYCAAHA4HAQGBiIgIKDLMyZcLhdsNhvNzc1KVWC1ubkZhYWFaGhowLBhw7ps0wBAVG6moaEB1tbWUk+kkJTudoUVP0skz+6xPSVetkrYB6qrf5/Pnz8Hi8VCTU2N6PMpw8Y/j8cDh8NBTU3NG9uflJaWIiQkBH/99Rd8fX2xfft2uXVu7queXpPF5eTkYPTo0cjPz8fw4cMldk1W6D2m8vJyeHl5wdnZGT/99NMrv7jCjbbq6mrRL8e6devwn//8R2GTH7pDIBDg+PHjiIiIgKamJqKiojBz5kzQaDQUFxfj6dOn4HK5osOjinI4sifq6upQWFgIgUAABoMhmiWIXxhe1xtKGdTX14PFYr2SwSde0cDMzAzDhg1T2OoLb9LQ0AAWi/XKjZF4kz8TExPY2NgozMHynhBvGGlqagojIyMMGTIEzc3NiIqKwo8//gg3NzfExcXBzs5O3sOVm59//hm+vr6ora3F4MGDJXZNVtjAVF5ejsmTJ8PKygoJCQmdgpKJiQmAF78cw4cPx9SpU7F27Vo8fPgQ33zzDXbv3q2Q6eI9xePxsHHjRvzwww8YOXIkBgwYADabjcTERDAYDKVoZPcmFEWJNvs1NDSgoaGB2traTj13lJn43oqw8ndtba3MM+2khaIoUQDm8/kYPHgw6uvroaurCwaD0S8qGnC5XCQkJCAqKgoeHh7Izs6Gnp4eYmNjMWvWLKVasuur5ORkpKamwsvLC7q6ukhOTkZgYCCmT5+OhIQEAJK7Jivs3PrKlStgs9lgs9kwNzfv9Jwwlurr6+Ovv/7CihUr4OzsDCMjI0RERPSLoAQA2traWL58OYqKivDrr78CAP7xj3/AxMRE6YMS8CJBYujQoeDxeHj06BEaGxthZGQEOp2u9EEJePH5hgwZAj6fL6rZp62tDWtra6UPSsCLz2dgYIBhw4YhPz8fT548gZqaGt57771+U3RUX18fLi4uMDY2xtWrV6GhoYG1a9di2rRp71RQAl7sx50+fRqRkZHg8/mg0+kIDAxEUFCQ6DWSuiYr7IyJAE6ePIlvvvkGc+fORUREBEpLSxEYGIiysjKsXr0aK1euVMplLuD/ziIVFxdDT08Ptra2GDhwINhsNqqrq2FpaQk6na4U+xJdEc+0AwAGg4HBgweLUrB7m+GmSMQ7Hgu75grbhWtoaIDBYCjtvgsAPHr0CGvXrsXly5cxb948bN++HampqdiwYQOePn2Ke/fuKd0+trIggUmBVVZWgsvldjoLIRAIcOTIEWzatAm6urrYtm0bpk+frjR3b+KHf9XV1UV9kcQ1NjaisLAQzc3NogrmipJl2B1vq2mnaDX4eqqlpQUcDgdVVVWwtLSEtbV1pxskgUAg6gOlp6endMt6z549Q3R0NA4ePIixY8ciLi4Ojo6Oouc7Ojpw8+ZNTJ48WW5j7O9IYFJSTU1NCA8Px5EjR+Dm5oaYmBiFPsz38gzC1tb2jWnRwrNZwv0ZaVRYkDTxEjvCjLU3zfjEM/hkUcKpr8SrJhgZGYHBYLyxTYp4IkRvqtjLmkAgwMmTJ7Fp0yZoampi+/btmD17tkL/m+uvSGBSchwOBwEBAaK01cjISIVbXqirqwObze5VjTnxCgu6urpgMpkKt3/R10w78QQC4c9HkS6GFEWhoqICbDYbmpqaYDKZPapIIT5DVMTEFoqikJaWhuDgYLBYLKxZswahoaFKu0zeH5DA1A9QFIWrV68iMDAQFRUVCAkJwbJly+S+P9PQ0CBqfd3Xs0htbW0oLi7Go0ePFCYNWXwG0ddMO4qiUF1dDTabLbUafL3xurT+3hA/CtDVEqA8VFRUIDQ0FL/99hu++OIL7NixQ1SpgJAf5Vm4l5KoqCi4u7tDS0vrtXeBZWVlmDFjBrS0tGBsbIzg4GC0t7fLdqBvQKPR4O3tjezsbERGRmLnzp0YN24c/vrrL5mW+Bfi8Xi4f/8+MjIyoKenB09PT9Dp9D4tU6mpqYHJZMLNzQ0CgQBJSUngcDgyb0EB/F8fqjt37qC+vh5OTk5wcHDo0zIVjUaDiYkJ3N3dYW5ujpycHNy9excNDQ0SHHn3NTc3IysrC/fv34epqSnc3d37XAZKW1sb9vb2cHFxQWNjI27fvo3i4mK5/B0+f/4cmzdvhqOjI6qqqpCUlIQTJ04ofVDi8/lwdHQEjUZDdnZ2p+cUuUXQy975GdPGjRsxaNAgPH78GEePHgWXy+30vEAggKOjI0xMTBAbG4vKykr4+vpi8eLF2Lp1q3wG/RYNDQ1Yv349jh49igkTJiA2NhYMBkPq7yvLJRsulytqQWFrawtTU1Opzy66yrSTVk07YUsPWWfwtba2gsPhoKKiQuqJGXV1dWCxWDJthS4QCPDLL78gMjISqqqq2LZtG+bOnSv3mamk+Pv7g8Vi4fLly8jKyhIlbShbi6B3PjAJxcfHIyAg4JXAdPnyZXz88ceoqKgQ3U0dOnQIa9euRU1NjUJnUxUUFCAgIADXrl3D119/jcjISKlkR8lrk1u4/MVisUQzKmmlJwsDoaS6x3aXeAacNAOFeCbd4MGDwWAwZPZ32JOkmL68T0ZGBkJCQpCXl4fAwECsW7dOKatuvM7ly5cRFBSEf//73xg1alSnwKRsLYLe+aW8t0lOToadnV2nKb6Pjw8aGxuRk5Mjx5G93fDhw3Hp0iWcP38eN2/ehJ2dHQ4ePCixpROBQCDqjNvY2AgXFxfY29vLLPNKfPnLxMQE9+7dQ1ZWFng8nsTeg8fjITs7G5mZmTA0NISHh4dEusd2l4aGBkaNGgVXV1c8f/4cd+7ckejyl7CleVJSEqqrq+Hg4ABHR0eZ/h0OHToUbm5usLKyQkFBAVJTU99au60nqqursWjRIkydOhU2NjbIz8/Hpk2b+lVQqq6uxuLFi3HixIkuZ9bJycmYOHFip5saHx8fFBQU4OnTp7Icarco5+lFGaqqqnpl3Vm8jLuio9FomD59Oj788EPs27cPUVFROHbsGLZv3w4vL69e3ZkKzyIJSwnJu42BqqoqrK2tYWZmBg6Hg5SUlD7PLvh8PoqKilBRUSFq0S3PC5mOjg7GjBkjyuB79OhRnzP4uFwuCgoKwOfzZbYc+joqKiowNzeHqakpHj16hPv374sOXve2z1VLS4uo+vX777+PmzdvYty4cf1m2U6Ioih8/fXXWLp0KVxcXFBSUvLKa5StRVC/nDH1prVGfzdgwAAEBgaCxWJhwoQJ+Pzzz/H555+Dw+F0+3sIm/8lJSWhtLQUI0aMwLhx4xSmjYG6ujpGjhzZaXZRUlLSo9lFe3s7OBwO7ty5g9bWVowfPx4jR45UmLtrAwMDjBs3DkwmE8XFxUhOTkZNTU2PklyePXuGe/fuITMzE0OGDIGHh4fCpKgLbzI8PT2hp6eHjIwM3Lt3r0ezYIqicO7cOYwZMwb/+te/cODAASQnJ8PV1VUhPmN3dfc6tm/fPjQ1NSEsLEzeQ5aYfrnH1Jsy7q/bY4qIiMDFixc7ZbgUFxdj2LBhyMzMxJgxYyQ5dJnJy8uDv78/bt26hUWLFiE8PPy154PEi3XKcqO6r+rr61FYWIi2tjYwGIw3ZpW93FK8p2d15EH8jFd3Wsy3tbWhqKgIjx8/hqmpKWxsbBQm4L5OTxNqsrKyEBwcjPv378Pf3x/h4eFyP1bQW929jn3xxRf47bffOv3bFggEUFVVxfz585GQkKB0LYL6ZWDqjbclP1RWVsLY2BgAcOTIEQQHB+PJkycK/4v9JhRF4Y8//sDq1avB5XKxfv16LFy4sFNat7C9QVNTE+h0utL0fRJ62+FQ8QoTgHQz7aTlbRl8ynBI+W3e1g6lpqYG69atw7lz5zBr1izs3LkTFhYWchyx7JSVlaGxsVH054qKCvj4+ODcuXNwdXWFubm50rUIeucDU1lZGerr63Hx4kXExsbi1q1bAF5kB+no6IjSxc3MzBATE4OqqiosWLAA3377rcKmi/dUe3s7du3ahejoaJibmyMmJga6uro4ePAgvvjiC1hZWSnEYci+EAgEKCkpEZXTsbW1RWtrqyjTbtiwYUpXk+9l4hl85ubmsLa2RkNDAwoLC6GiogImkwlDQ0OlCrovEzaQzMrKAofDQVBQEI4ePYq4uDgwmUzExcWJutG+q0pKSkCn0ztl5Slbi6B3PjB9/fXXol4i4q5duyYq0lhaWoply5bh+vXr0NbWhp+fH6Kjo+VeWUHS6urqsHLlSvzyyy8AgGnTpuHIkSMKN83vi5aWFhQUFODJkyeg0WiwsrJS6irmXWlubkZubi4aGhqgqqoKW1tbmWYSysK1a9ewdOlSVFZWQkdHB7Gxsfjmm2+UajYvLV0FJuDFAdsVK1YgPT0dRkZGWLVqFdauXSu/gb7BOx+YiBfq6+uxbds2HDhwAJMmTcLTp09x//59LFmyBOvWrYOOjo68h9hn4pl2RkZGaG1tBY/Hw7Bhw/rNhbulpUXUOsTY2Bg8Hg+tra0KWYOvtx48eIDg4GBkZGTgww8/RH5+Pjo6OrBlyxbMmTOnX/w9vuv6z20i0Sc3btzAgwcPcPv2bTg5OYGiKFy4cAEhISE4c+YMNmzYAF9fX6W8I325pt348eOhra0NiqJQW1srSr9Wxv0lofb2dtFSpbGxMdzd3aGpqdmpBl9ZWRlsbW0VogZfb9TW1iI8PBynT5/GjBkzkJeXBysrK7S3tyM+Ph47d+7EzJkzlbrHFfECmTERAF4kAXR1sWpra0NsbCxiY2NhZWWF2NhYpVnD726mnfB1HA4HOjo6YDKZStM/SPxMmZaWFphMZpeZeT3N4FMkbW1t2LNnD3bv3g1ra2vs2bMHEyZMeOXf4Ov+DRPKhwQmoltqamoQHByMU6dOYfr06di+fbvCZj29nGnX3TI34qWVhg4dCltbW4VONa6trUVhYSE6OjrAZDK7Ndt7uXyUInfRpSgKly5dQlhYGJ49e4ZNmzaRfaR3BAlMRI9kZWUhICAAGRkZWLZsGcLCwt7YLE7WxGva9TbT7vnz52Cz2Xjy5IkoI1GRkiOamprAYrHQ0NCAYcOGwcLCosef8eUMPjqdrlB1H3NzcxEcHIzU1FQsW7YMmzZt6hf7nET3kF1CGTpw4ACsra2hoaEBV1dXpKWlyXtIPTZmzBhcv34d8fHx+M9//gN7e3ucOHFCLq0LxPF4PFE1A2FNu95csAFAU1MTdnZ2cHFxwdOnT3Hnzh08fvxYLi1ExPH5fOTm5iItLQ3a2trw9PSElZVVrz6jsAbfuHHj8OzZM4nX4Out+vp6rFy5Eh4eHtDT08PDhw+xc+dOpQ1Kn3zyCSwtLaGhoQFTU1MsWLAAFRUVnV6jTO0oZIXMmGTkzJkz8PX1xaFDh+Dq6oq4uDicPXsWBQUFooO7yqa1tRXR0dHYuXMnGAwGYmNjZV6L7OWadj3tHvs2wurXLBYLqqqqorNAsiQQCFBaWoqSkhIYGhqCwWBIfPlNWCVDXhl87e3t2L9/P3bs2AFzc3PExcX1upajItm9ezfc3NxgamqK8vJyrFmzBgCQlJQEQPnaUcgKCUwy4urqirFjx2L//v0AXmxGW1hYYNWqVQgNDZXz6Pqmuroaa9aswS+//IKZM2ciOjoaZmZmUn1P8Uw7AwMDqbdpEDYHLCoqgr6+PphMptTv4oWVv9lsNgYOHAgmkynVM2XiGXyqqqpgMBhSP5BLURT+/PNPhIWFgcvlIjIyEkuWLOm3+0gXL17Ep59+Cj6fDzU1NaVrRyErZClPBlpbW3H37l14e3uLHlNRUYG3tzeSk5PlODLJGDp0KE6cOIGkpCRUVVXB0dERmzdvRktLi8TfS5hddufOHdTV1cHJyUkmbRpUVFRgZWUFDw8PaGtrIzU1Fbm5ueDz+VJ5v/r6eqSmpoLD4YDBYGDcuHFSP+gs3kbEzMwMDx8+RGZmZqdyN5JUUFCAWbNmYf78+Zg2bRpYLBaWLVvWb4NSfX09fv75Z7i7u4uqqChbOwpZIYFJBmprayEQCLpsn6EMrTO6y9nZGbdu3cKPP/6I06dPw8HBAadOnZLIvoVwSS05OVlU2Xzs2LEyL7Sqrq6O4cOHY/z48WhtbZX43oyw/1N2djaGDh0Kd3d3mbejEA/Cenp6SE9Px4MHD/D8+XOJfP+GhgYEBATAzc0NAwcOxIMHD7Bnzx6lSdHvqbVr10JbWxuGhoYoKyvDr7/+KnpO2dvqSAsJTIRE0Wg0fPXVVygoKMDChQsRGBgIb29vpKen9zp5gMvlIiMjA3l5ebC0tISbm9sbK4XLgra2NhwdHeHo6Ijq6mokJSWhsrKy15+xtbUV+fn5SElJwcCBA+Hp6Qk6nS7X2YOamhoYDAY8PDygoqKCpKQkFBQUoLW1tVffT7iPZGdnh6SkJFy8eBG///47bG1tJTxy6eppW53g4GBkZWXhr7/+gqqqKnx9feWeSKPoyB6TDLS2tkJLSwvnzp3Dp59+Knrcz88PXC630x1Uf1NRUYE1a9bg3//+Nz777DNs27btlTvE1+HxeGCz2airq4OVlRWsrKwUKm1bSNinisVi9XgvqKOjA2VlZSguLsagQYPAYDAUNgNNPE3d2toalpaW3QqcFEXh6tWrCA0NRU1NDcLDw7FixQqlXbLrTVsdocePH8PCwgJJSUlwc3NTunYUsqJ4v+X9kLq6OpydnXH16lVRYOro6MDVq1excuVK+Q5OyszMzPDzzz/D398f/v7+cHBwwKpVq7BmzZrXZs+JZ9qZmprKvXvs29BoNJiamsLY2BhlZWXIysqCoaEhbG1tX7v39XK2n7y7AHeHrq4unJycRBl8jx49emvnWzabjZCQEFy/fh2LFi1CVFSUwve5epshQ4ZgyJAhvfp/Ozo6AEC0N+nm5ob169ejra1NtO905coVDB8+/J0NSgCZMcnMmTNn4Ofnh8OHD2PcuHGIi4vDL7/8gvz8/G7PIJQdRVE4ceIE1q9fD1VVVWzevBmzZ88WXdRezrQTth5RNnw+HxwOB5WVlTA3N8ewYcM6tQwRPwRsa2urlMVVhbNENpuNAQMGvJLB19jYiM2bN+P48eOYOHEi9uzZgxEjRsh51LKVmpqK9PR0eHp6YvDgweBwOAgPD0d1dTVycnIwcOBApWtHISskMMnQ/v37ERsbK8pc27t3L1xdXeU9LJlraWnB5s2bsXfvXtjb22Pr1q24efMmCgoKsHjxYjAYjH5xt9jc3IzCwkJRhQYjIyNRsztra2uFXZrsCWEafU5ODg4cOICQkBDk5+dj27ZtMDAwwK5duzB9+nSlC7yS8ODBA/j7+4taw5uammLatGnYsGED3nvvPdHrlKkdhayQwETITVlZGb766iskJSVBW1sbkZGRWLZsWb+7iFVXVyMvLw9tbW0YPHgwRo0apVBlnCShrq4OS5cuxeXLl6GmpoY1a9Zg06ZNSh94CfkgWXmEXKSkpGD+/PlgsVgICgrC8OHD8c9//hMxMTFSOxska8LZRF5eHnR0dECn08Hj8fDw4UM0NDTIe3gSU1xcjKVLl+K///0v5s6di9mzZ2PXrl0ICQl5a5IAQXSF3M4QcnHx4kV4eXnh0qVL0NXVhUAgQEJCAsLDw3HixAls2bIFs2bNUsrZk3ifJ4qiMGrUKFEPJGtra5SUlCAjIwPGxsawtbVV2tlTU1MToqKi8L//+79wd3dHZmYmRo0aBeDF8lRoaKioBxZB9ARZyiMUyrNnzxAZGYkDBw7A2dkZsbGxsLOzk/ewuq2pqQmFhYVoamqCjY3Na6ubi3eatbS0VKr27gKBAPHx8diyZQv09fWxY8cOzJw5UylvIgjFRAIToZBKS0sRGBiIP/74A3PnzsWWLVsU+s5bvI2EhYUF6HR6p0y812lsbERhYSGam5vfGMgUAUVRuHPnDoKDg/Ho0SOEhoYiKChIaQIqoTxIYCIUFkVRuHXrFvz9/VFSUoKgoCB8//333brgy4pAIBC1NDcyMgKDwejx0px4Y0MajQYGg6Fw7c/Lysqwdu1aJCYm4n/+538QExOj0DcKhHJTzFszottu3ryJmTNnis7CiJ8gB15c9CIiImBqagpNTU14e3uLOrsqOhqNhokTJyIjIwPbt2/HDz/8AGdnZ/zxxx9yL+lCURTKy8tx+/ZtUTFZe3v7Xu0X0Wg0GBsbw83NDebm5sjJyUFmZiaampqkMPKeefbsGdavXw8nJyc0NjYiLS0NR48eVdqgVFJSgkWLFoFOp0NTUxM2NjbYuHHjK2WWSI8k+SKBScnxeDw4ODjgwIEDXT4fExODvXv34tChQ0hNTYW2tjZ8fHykUvlbWlRVVbFkyRKwWCx88skn8PPzw8cff4zc3Fy5jKeurg4pKSkoLi7G8OHDJVZMVkVFBZaWlvDw8ICuri7S0tKQk5MjlyxFYTKKvb09fvvtN/z000/4+++/4eDgIPOxSFJ+fj46Ojpw+PBh5OTkYPfu3Th06BDWrVsnek1jYyOmTp0KKysr3L17F7GxsYiMjMSRI0fkOPJ3C1nK60doNBrOnz8vKntEURTMzMywevVqUYOyhoYGDB06FPHx8Zg7d64cR9t7HA4HQUFBSExMxPz587F582YYGBhI/X2bm5vBYrHA5XJBp9NhaWkp1f2gZ8+egc1mo7a2VtTiXdr15SiKQkpKCoKDg1FUVITg4GCEhIQo1PKppMXGxuLgwYMoKioCANIjSQGQGVM/VlxcjKqqqk59oPT19eHq6qrUfaBsbGxw4cIFXLp0CRkZGbC3t8eePXvQ3t4ulfdrbW1FXl4eUlNToampCQ8PD1hbW0s9SUFLSwv29vZwcnJCXV0d7ty5g4qKCqktY5aXl2PBggX46KOPYGdnh8LCQqxfv75fByXgxc2a+I0N6ZEkfyQw9WPCfi79sQ8UjUbDlClTkJWVhS1btiAuLg5jx45FYmKixC7cAoEAxcXFuHPnDvh8PsaPH48RI0Z0WTVamgYNGoSxY8eCyWSCw+EgNTUV9fX1Evv+z58/x8aNGzFmzBjU1NQgOTkZCQkJMDY2lth7KCo2m419+/bhu+++Ez1GeiTJHwlMhFJTVVXF8uXLwWKx4OPjg/nz52PWrFkoKCjo9fcUtjRPSkpCdXU1HBwcZNIl903Eu8uamJjg3r17yMrKAo/H6/X3FAgEOHnyJBwcHHDu3DkcPXoUN2/ehJOTkwRHLhs97ZEEvJghTps2DXPmzMHixYvlNHKiKyQw9WMmJiYAXtRqE1ddXS16rr/Q09PD3r178eDBAwwcOBBubm4ICAjocekfLpeLtLQ0sFgs2NjYwNXVVSb7V92lqqoKa2treHh4QENDAykpKcjPz+9R8z6KopCeno4PPvgAq1evxuLFi5Gfn48vv/xSoVLUe2L16tXIy8t749ewYcNEr6+oqICXlxfc3d1fSWowMTHp8ndG+BwhfeRkXD9Gp9NhYmKCq1evwtHREcCLjKPU1FQsW7ZMvoOTEltbW/z++++4cuUKgoKCYGdnh7Vr1+K7775740HQZ8+egcVioa6uTlT5W5Eb2amrq2PkyJGwsLAAi8XCnTt3QKfTYWFh8cZxV1ZWIiwsDL/++ivmzJmDixcvwtTUVIYjl46e9EgqLy+Hl5cXnJ2dcfz48Vf2CkmPJPkjWXlKrrm5GWw2GwAwZswY7Nq1C15eXjAwMIClpSW2b9+O6OhoJCQkgE6nIzw8HPfv30dubi40NDTkPHrpEggE2L9/P7Zs2YIhQ4YgOjoaU6ZM6TQraGtrQ1FRER4/fgxTU1PY2NgodFPC16mrq0NhYSHa29vBYDBeaT3f0tKC2NhY7Nu3T5Qs4uLiorQzpN4qLy/H5MmTYWVlhYSEhE5BXDgbIj2S5I8EJiV3/fp1eHl5vfK4n58f4uPjQVEUNm7ciCNHjoDL5cLT0xM//PADmEymHEYrH0+fPsX69etx7NgxTJ48GTExMTA3N8ehQ4dgZ2eHQYMGgclkQldXV95D7ROKolBRUQE2m43Hjx9j6NChmDJlCs6ePYuIiAjQaDRs3boV8+bNe+cCklB8fDwWLlzY5XPil0LSI0m+SGAi3hn5+fn4/vvvcf36dWhoaGDQoEE4ffo0HBwc+tWFWiAQYM+ePdiyZQu0tLTA5/MRFBSE9evX9/tZMtE/kOQH4p3R3NwMPp8PbW1taGlp4fnz50hNTUVHR4e8hyZRdXV1yM3NRUdHB/T19SEQCNDS0qJU1T6IdxsJTMQ7ISUlBRMnToSHhwdKS0vx+PFjhIaGYsuWLXBzc8P169flXn+vr/h8PrZt2wZ7e3uw2WzcuHEDRUVFSElJQVZWFjw9PZX+MxLvBrKUR7wThPsv7733XqfH6+vrERoaioSEBHh7eyMmJgZ0Ol1Oo+wdiqJw/vx5hIeHo729HVFRUZg/f36njf3XfX6CUERkxkS8E2g0WpcXZQMDAxw5cgSZmZng8/kYO3YsQkND0dzcLIdR9tz9+/fh4+ODpUuXYu7cuSgsLISvr+8rKeOv+/wEoYhIYCIIAKNGjcKVK1dw+vRpJCYmwt7eHseOHYNAIJD30LpUU1OD7777DpMmTYKJiQlyc3Oxbds2pW3TThDiSGAium3btm0YO3YsdHV1YWxsjE8//fSV0j8tLS1YsWIFDA0NoaOjg9mzZ79yil5R0Wg0fPLJJ8jJyUFAQAAiIiLg6emJ27dvK8zeTFtbG2JjY+Hg4IDc3Fz897//xdmzZ2FpaSnvofVaVFQU3N3doaWl9dr2IWVlZZgxYwa0tLRgbGyM4OBgqRXtJeSPBCai227cuIEVK1YgJSUFV65cQVtbG6ZOndqpXltgYCB+++03nD17Fjdu3EBFRQX+8Y9/yHHUPaempobQ0FAUFhbC2dkZM2fOxLx581BWVia3MVEUhYsXL8LJyQmHDx/Gzp07kZaWhgkTJih9qntrayvmzJnz2mokAoEAM2bMQGtrK5KSkpCQkID4+HhERETIeKSEzFAE0UtPnjyhAFA3btygKIqiuFwupaamRp09e1b0mry8PAoAlZycLK9h9ll2djY1efJkSlNTkwoMDKRqamooHo8ns6+0tDRq4sSJlKamJrVmzRqqublZ3j8SqTh+/Dilr6//yuOXLl2iVFRUqKqqKtFjBw8epPT09Cg+ny/DERKyQmZMRK8JC6QKi5zevXsXbW1tnfo/jRgxApaWlkrd/8nBwQF///03Tpw4gV9//RX29vb417/+JfX9p7q6OqxYsQKenp4YPHgwcnNzERsbK9cq5/KQnJwMOzu7Tq0ofHx80NjYiJycHDmOjJAWEpiIXuno6EBAQAA8PDwwevRoABB1/Hx5n6C/9H+aPXs28vPzsXz5coSGhmLSpElITk6W+P5TW1sbdu/eDQcHB2RlZSExMRHnz5+HtbW1RN9HWZD+SO8eEpiIXlmxYgUePnyI06dPy3soMqWmpoYNGzagsLAQdnZ2+Oijj7BgwQKUl5f3+XtTFIVLly7BxcUF+/btw9atW3H37l14eXkp3T5Sb/ojEYQQCUxEj61cuRK///47rl27BnNzc9HjJiYmaG1tBZfL7fT6/tj/ydjYGAkJCUhOTkZNTQ3GjBmDyMhIPH/+vFffLy8vD5988gl8fX3x8ccfg8ViYenSpQrdeuNNetof6U1If6R3kLw3uQjl0dHRQa1YsYIyMzOjCgsLX3lemPxw7tw50WP5+flKn/zwNh0dHdSpU6coa2tr6r333qN+/PFHqrGxsVuJDY8fP6a+/fZbSl1dnfr4448pNpst748jN29LfqiurhY9dvjwYUpPT49qaWmR4QgJWSGBiei2ZcuWUfr6+tT169epyspK0dezZ89Er1m6dCllaWlJ/f3331RGRgbl5uZGubm5yXHUstPS0kJt3LiR0tPTo1xcXKjr169Tzc3NXQakhoYGKjo6mjIwMKBGjx5NXblyhero6JD3R5CL0tJSKisri9q0aROlo6NDZWVlUVlZWVRTUxNFURTV3t5OjR49mpo6dSqVnZ1NJSYmUkOGDKHCwsLkPHJCWkhgIroNQJdfx48fF73m+fPn1PLly6nBgwdTWlpa1GeffUZVVlbKb9ByUFFRQc2bN49SV1en5syZQ7HZbFFAam5ups6fP08NHz6cMjY2pvbt20e1t7fLe8hy5efn1+W/q2vXroleU1JSQk2fPp3S1NSkjIyMqNWrV1NtbW3yGzQhVaSIK0FIAUVRyMjIgL+/P+7evYuJEydi+fLlWL58Oerr67FkyRJERUVBT09P3kMlCIVDAhNBSBFFUVi9ejXi4uJAURQMDAxw/vx5TJw4Ud5DIwiFRbLyCEJKKIrCyZMncfbsWTg5OcHb2xsURWH79u2v1BgkCOL/kBkTQUhJVVUVJk2ahPDwcMybNw8qKiqoq6tDZGQkamtrcerUKXkPkSAUEglMBCFFHR0dUFF5dWHidY8TBEECE0EQBKFgyC0bQRAEoVBIYCIU2sGDB2Fvbw89PT3o6enBzc0Nly9fFj2vzI0JFdmBAwdgbW0NDQ0NuLq6Ii0tTd5DIt4hJDARCs3c3BzR0dG4e/cuMjIy8MEHH2DWrFmidgf9oTGhojlz5gyCgoKwceNGZGZmwsHBAT4+Pnjy5Im8h0a8I8geE6F0DAwMEBsbi88//xxDhgzByZMn8fnnnwMA8vPzMXLkSCQnJ2P8+PFyHqlycnV1xdixY7F//34ALxI1LCwssGrVKoSGhsp5dMS7gMyYCKUhEAhw+vRp8Hg8uLm59dvGhPLU2tqKu3fvdvqZqqiowNvbm/xMCZkZIO8BEMTbPHjwAG5ubmhpaYGOjg7Onz+P999/H9nZ2f22MaG81NbWQiAQdNmYj/RPImSFBCZC4Q0fPhzZ2dloaGjAuXPn4Ofnhxs3bsh7WARBSAkJTITCU1dXh62tLQDA2dkZ6enp2LNnD7788ktRY0LxWVN/bEwoK0ZGRlBVVe2yMR/5mRKyQvaYCKXT0dEBPp8PZ2dnqKmp4erVq6LnCgoKUFZWBjc3NzmOUHmpq6vD2dm508+0o6MDV69eJT9TQmbIjIlQaGFhYZg+fTosLS3R1NSEkydP4vr16/jzzz+hr6+PRYsWISgoCAYGBtDT08OqVavg5uZGMvL6ICgoCH5+fnBxccG4ceMQFxcHHo+HhQsXyntoxDuCBCZCoT158gS+vr6orKyEvr4+7O3t8eeff+LDDz8EAOzevRsqKiqYPXs2+Hw+fHx88MMPP8h51Mrtyy+/RE1NDSIiIlBVVQVHR0ckJia+khBBENJCzjERBEEQCoXsMREEQRAKhQQmgiAIQqGQwEQQBEEoFBKYCIIgCIVCAhNBEAShUEhgIgiCIBQKCUwE8RbR0dGg0WgICAgQPSaNBoU3b97EzJkzYWZmBhqNhgsXLnR6nqIoREREwNTUFJqamvD29gaLxerTexKEIiKBiSDeID09HYcPH4a9vX2nx6XRoJDH48HBwQEHDhzo8vmYmBjs3bsXhw4dQmpqKrS1teHj44OWlpY+vS9BKByKIIguNTU1UQwGg7py5Qo1adIkyt/fn6IoiuJyuZSamhp19uxZ0Wvz8vIoAFRycrJE3hsAdf78edGfOzo6KBMTEyo2Nlb0GJfLpQYOHEidOnVKIu9JEIqCzJgI4jVWrFiBGTNmdGqaB0AuDQqLi4tRVVXV6T319fXh6upKGvgR/Q6plUcQXTh9+jQyMzORnp7+ynNVVVUyb1Ao/L5dNfAjTRGJ/oYEJoJ4yaNHj+Dv748rV65AQ0ND3sMhiHcOWcojiJfcvXsXT548gZOTEwYMGIABAwbgxo0b2Lt3LwYMGIChQ4eKGhSKk2YzPeH3JQ38iHcBCUwE8ZIpU6bgwYMHyM7OFn25uLhg/vz5ov+WdYNCOp0OExOTTu/Z2NiI1NRU0sCP6HfIUh5BvERXVxejR4/u9Ji2tjYMDQ1Fj0ujQWFzczPYbLboz8XFxcjOzoaBgQEsLS0REBCALVu2gMFggE6nIzw8HGZmZvj00097/Z4EoYhIYCKIXpBGg8KMjAx4eXmJ/hwUFAQA8PPzQ3x8PEJCQsDj8bBkyRJwuVx4enoiMTGR7IMR/Q5pFEgQBEEoFLLHRBAEQSgUEpgIgiAIhUICE0EQBKFQSGAiCIIgFAoJTARBEIRCIYGJIAiCUCgkMBEEQRAKhQQmgiAIQqGQwEQQBEEoFBKYCIIgCIVCAhNBEAShUEhgIgiCIBTK/wNyWXU2GgyHTwAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "model.eval()\n",
+    "\n",
+    "def draw_results(timestep, save=False):\n",
+    "    with torch.no_grad():\n",
+    "        input_pos = dataset[0].pos\n",
+    "        padded_pos = torch.zeros(31*12, 3)\n",
+    "        padded_pos[:31] = input_pos\n",
+    "\n",
+    "        velo = dataset[0].velo\n",
+    "        padded_velo = torch.zeros(31*12, 3)\n",
+    "        padded_velo[:31] = velo\n",
+    "\n",
+    "        input_edge_index = dataset[0].edge_index\n",
+    "\n",
+    "        res = model(None, padded_pos, input_edge_index, torch.zeros(input_edge_index.shape[1], 1), padded_velo)\n",
+    "\n",
+    "        pos = res[1][timestep].detach()[:31]\n",
+    "        edge_index = input_edge_index\n",
+    "\n",
+    "    fig = plt.figure()\n",
+    "    ax = fig.add_subplot(111, projection='3d')\n",
+    "\n",
+    "    ax.set_xlim3d(-50, 10)\n",
+    "    ax.set_ylim3d(-20, 40)\n",
+    "    ax.set_zlim3d(-20, 40)\n",
+    "\n",
+    "    xs, ys, zs = [], [], []\n",
+    "    for i in range(pos.shape[0]):\n",
+    "        coord = pos[i]\n",
+    "        xs.append(coord[0])\n",
+    "        ys.append(coord[1])\n",
+    "        zs.append(coord[2])\n",
+    "    ax.plot(zs, xs, ys, 'b.')\n",
+    "\n",
+    "    for i in range(input_edge_index.shape[1]):\n",
+    "        xs = [pos[input_edge_index[:,i][0]][0], pos[input_edge_index[:,i][1]][0]]\n",
+    "        ys = [pos[input_edge_index[:,i][0]][1], pos[input_edge_index[:,i][1]][1]]\n",
+    "        zs = [pos[input_edge_index[:,i][0]][2], pos[input_edge_index[:,i][1]][2]]\n",
+    "        ax.plot(zs, xs, ys, 'r')\n",
+    "    \n",
+    "    expected_pos = dataset[0].output_pos[:,timestep,:]\n",
+    "\n",
+    "    for i in range(pos.shape[0]):\n",
+    "        coord = expected_pos[i]\n",
+    "        xs.append(coord[0])\n",
+    "        ys.append(coord[1])\n",
+    "        zs.append(coord[2])\n",
+    "    ax.plot(zs, xs, ys, 'g.')\n",
+    "\n",
+    "    for i in range(input_edge_index.shape[1]):\n",
+    "        xs = [expected_pos[input_edge_index[:,i][0]][0], expected_pos[input_edge_index[:,i][1]][0]]\n",
+    "        ys = [expected_pos[input_edge_index[:,i][0]][1], expected_pos[input_edge_index[:,i][1]][1]]\n",
+    "        zs = [expected_pos[input_edge_index[:,i][0]][2], expected_pos[input_edge_index[:,i][1]][2]]\n",
+    "        ax.plot(zs, xs, ys, 'y')\n",
+    "    \n",
+    "\n",
+    "    ax.view_init(elev=30, azim=45)  # Optional: adjust view angle\n",
+    "    if save:\n",
+    "        plt.savefig(f\"time_{timestep}.png\", dpi=300, bbox_inches='tight')\n",
+    "        plt.close()\n",
+    "    else:\n",
+    "        plt.show()\n",
+    "for time in range(5):\n",
+    "    draw_results(time)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/pina/egno_data/egno_training.ipynb b/pina/egno_data/egno_training.ipynb
new file mode 100644
index 00000000..9c8f046a
--- /dev/null
+++ b/pina/egno_data/egno_training.ipynb
@@ -0,0 +1,204 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "d19e50a5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%load_ext autoreload"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "4c55a527",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%autoreload 1\n",
+    "%aimport pina.model.block.egno_block\n",
+    "\n",
+    "from pina import Trainer\n",
+    "from pina.solver import SupervisedSolver\n",
+    "from pina.adaptive_function import AdaptiveSIREN\n",
+    "from pina.problem.zoo import SupervisedProblem\n",
+    "from pina.graph import Graph\n",
+    "from pina.model.block.egno_block import EquivariantGraphNeuralOperatorBlock, timestep_embedding\n",
+    "import amc_parser as amc\n",
+    "import numpy as np\n",
+    "import torch"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "id": "09cde90a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Data\n",
+    "def Generate_input(timesteps):\n",
+    "    asf_name = 'run.asf'\n",
+    "    joints = amc.parse_asf(asf_name)\n",
+    "    joints['root'].get_name_to_idx()\n",
+    "    edge_index = joints['root'].output_edges()\n",
+    "    edge_index = torch.tensor(edge_index).T  # Shape: [2, num_edges]\n",
+    "\n",
+    "    train_case_id = [1, 2, 5, 6, 10]\n",
+    "    val_case_id = [0, 4, 9]\n",
+    "    test_case_id = [3, 7, 8]\n",
+    "\n",
+    "    train_mapping = {}\n",
+    "    for i in train_case_id:\n",
+    "        sampled = np.random.choice(np.arange(100), size=48, replace=False)\n",
+    "        train_mapping[i] = sampled\n",
+    "\n",
+    "    all_graphs = {}\n",
+    "    input_data = []\n",
+    "    output_data = []\n",
+    "\n",
+    "    for i in train_case_id:\n",
+    "        amc_name = f'{i}.amc'\n",
+    "        motions = amc.parse_amc(amc_name)\n",
+    "        graphs = []\n",
+    "\n",
+    "        for time, motion in enumerate(motions):\n",
+    "            joints['root'].set_motion(motion)\n",
+    "            node_coords = joints['root'].output_coord()\n",
+    "            # Convert to tensor and ensure correct shape [num_nodes, 3]\n",
+    "            pos = torch.tensor(node_coords, dtype=torch.float32)\n",
+    "\n",
+    "            graph = Graph(pos=pos, edge_index=edge_index, time=torch.tensor(time, dtype=torch.float32))\n",
+    "            graphs.append(graph)\n",
+    "\n",
+    "        for index in range(0, len(graphs)-1, 1):\n",
+    "            graphs[index]['velo'] = graphs[index+1]['pos']-graphs[index]['pos']\n",
+    "        # duplicate last for finite differences\n",
+    "        graphs[-1]['velo'] = graphs[-2]['velo']\n",
+    "            \n",
+    "        all_graphs[i] = graphs\n",
+    "\n",
+    "        for step in train_mapping[i]:\n",
+    "            # Input: single graph\n",
+    "            input_data.append(graphs[step])\n",
+    "            \n",
+    "            # Output: sequence of future graphs\n",
+    "            for jump in range(1, timesteps + 1):\n",
+    "                output_data.append(graphs[step + jump])\n",
+    "\n",
+    "    return input_data, output_data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "id": "a0e86f3c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def apply_time_embeds(in_data, n_timesteps, embedding_dim):\n",
+    "    '''\n",
+    "    Data: List of Graphs\n",
+    "    '''\n",
+    "    output = []\n",
+    "    timesteps = timestep_embedding(n_timesteps, embedding_dim)\n",
+    "\n",
+    "    for graph in in_data:\n",
+    "        pos = graph.pos\n",
+    "        edge_index = graph.edge_index\n",
+    "        velo = graph.velo\n",
+    "        time = graph.time\n",
+    "\n",
+    "        for i in range(n_timesteps):\n",
+    "            x = timesteps[i].repeat(pos.shape[0],1)\n",
+    "            graph = Graph(x= x, pos=pos, edge_index=edge_index, velo=velo, time=time)\n",
+    "            output.append(graph)\n",
+    "    return output"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 85,
+   "id": "71ed0f80",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "n_timesteps = 5\n",
+    "embedding_dim = 32\n",
+    "\n",
+    "in_data, out_data = Generate_input(n_timesteps)\n",
+    "in_data = apply_time_embeds(in_data, n_timesteps, embedding_dim)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 86,
+   "id": "191ec7d4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Define Problem\n",
+    "problem = SupervisedProblem(in_data, out_data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "26fd5182",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Define Model\n",
+    "def eq_activation(layer):\n",
+    "    eps = 1e-6\n",
+    "    norm = torch.norm(layer, dim=-1, keepdim=True)\n",
+    "    return torch.tanh(norm) * layer\n",
+    "\n",
+    "model = EquivariantGraphNeuralOperatorBlock(n_layers = 2,\n",
+    "        time_discretizations = n_timesteps,\n",
+    "        num_scalar_features = 1,\n",
+    "        num_vec_features = 2,\n",
+    "        num_edge_features = 0,\n",
+    "        embedding_dim = embedding_dim,\n",
+    "        n_nodes = 31,\n",
+    "        n_modes= 6,\n",
+    "        eq_activation = eq_activation,\n",
+    "        scalar_feature_activation = torch.relu)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "e60bc7a8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# solver = SupervisedSolver(problem, model)\n",
+    "# trainer = Trainer(solver=solver, max_epochs=1000, accelerator=\"cpu\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/pina/egno_data/preprocess.py b/pina/egno_data/preprocess.py
new file mode 100644
index 00000000..3f7b56a2
--- /dev/null
+++ b/pina/egno_data/preprocess.py
@@ -0,0 +1,31 @@
+import amc_parser as amc
+import torch
+from pina.graph import Graph, LabelBatch
+
+
+asf_name = 'walk.asf'
+amc_name = 'walk.amc'
+
+
+joints = amc.parse_asf(asf_name)
+motions = amc.parse_amc(amc_name)
+
+
+joints['root'].get_name_to_idx()
+edge_index = joints['root'].output_edges()
+edge_index = torch.tensor(edge_index).T
+
+
+joints['root'].set_motion(motions[0])
+node_coords = joints['root'].output_coord()
+# [Nodes, 3]
+pos = torch.tensor(node_coords)
+
+data_set = []
+
+Graph(edge_index = edge_index, pos = pos)
+
+
+
+
+
diff --git a/pina/egno_data/run.asf b/pina/egno_data/run.asf
new file mode 100644
index 00000000..d02628e9
--- /dev/null
+++ b/pina/egno_data/run.asf
@@ -0,0 +1,338 @@
+# AST/ASF file generated using VICON BodyLanguage
+# -----------------------------------------------
+:version 1.10
+:name VICON
+:units
+  mass 1.0
+  length 0.45
+  angle deg
+:documentation
+   .ast/.asf automatically generated from VICON data using
+   VICON BodyBuilder and BodyLanguage model FoxedUp or BRILLIANT.MOD
+:root
+   order TX TY TZ RX RY RZ
+   axis XYZ
+   position 0 0 0  
+   orientation 0 0 0 
+:bonedata
+  begin
+     id 1 
+     name lhipjoint
+     direction 0.625129 -0.738222 0.253461 
+     length 2.5165 
+     axis 0 0 0  XYZ
+  end
+  begin
+     id 2 
+     name lfemur
+     direction 0.34202 -0.939693 0  
+     length 7.54288  
+     axis 0 0 20  XYZ
+    dof rx ry rz
+    limits (-160.0 20.0)
+           (-70.0 70.0)
+           (-60.0 70.0)
+  end
+  begin
+     id 3 
+     name ltibia
+     direction 0.34202 -0.939693 0   
+     length 7.71864   
+     axis 0 0 20   XYZ
+    dof rx
+    limits (-10.0 170.0)
+  end
+  begin
+     id 4 
+     name lfoot
+     direction 0.0772037 -0.212115 0.97419   
+     length 2.18429   
+     axis -90 7.62852e-016 20   XYZ
+    dof rx rz
+    limits (-45.0 90.0)
+           (-70.0 20.0)
+  end
+  begin
+     id 5 
+     name ltoes
+     direction 1.53513e-011 -4.21834e-011 1   
+     length 1.09718   
+     axis -90 7.62852e-016 20   XYZ
+    dof rx
+    limits (-90.0 20.0)
+  end
+  begin
+     id 6 
+     name rhipjoint
+     direction -0.621946 -0.740623 0.254285   
+     length 2.50835  
+     axis 0 0 0  XYZ 
+  end
+  begin
+     id 7 
+     name rfemur
+     direction -0.34202 -0.939693 0   
+     length 7.43366   
+     axis 0 0 -20   XYZ
+    dof rx ry rz
+    limits (-160.0 20.0)
+           (-70.0 70.0)
+           (-70.0 60.0)
+  end
+  begin
+     id 8 
+     name rtibia
+     direction -0.34202 -0.939693 0   
+     length 7.77122   
+     axis 0 0 -20   XYZ
+    dof rx
+    limits (-10.0 170.0)
+ end
+ begin
+     id 9 
+     name rfoot
+     direction -0.082694 -0.2272 0.970331   
+     length 2.2293  
+     axis -90 -7.62852e-016 -20   XYZ
+    dof rx rz
+    limits (-45.0 90.0)
+           (-20.0 70.0)
+  end
+  begin
+     id 10 
+     name rtoes
+     direction -1.53565e-011 -4.21695e-011 1   
+     length 1.1178   
+     axis -90 -7.62852e-016 -20   XYZ
+    dof rx
+    limits (-90.0 20.0)
+  end
+  begin
+     id 11 
+     name lowerback
+     direction -0.000695208 0.988391 -0.151929   
+     length 1.95077   
+     axis 0 0 0   XYZ
+    dof rx ry rz
+    limits (-20.0 45.0)
+           (-30.0 30.0)
+           (-30.0 30.0)
+  end
+  begin
+     id 12 
+     name upperback
+     direction -0.00268925 0.999885 -0.0149203   
+     length 1.96246   
+     axis 0 0 0   XYZ
+    dof rx ry rz
+    limits (-20.0 45.0)
+           (-30.0 30.0)
+           (-30.0 30.0)
+  end
+  begin
+     id 13 
+     name thorax
+     direction -0.000848408 0.998481 0.0550912   
+     length 2.00867   
+     axis 0 0 0   XYZ
+    dof rx ry rz
+    limits (-20.0 45.0)
+           (-30.0 30.0)
+           (-30.0 30.0)
+  end
+  begin
+     id 14 
+     name lowerneck
+     direction -0.00577709 0.998999 0.0443608   
+     length 1.75762   
+     axis 0 0 0   XYZ
+    dof rx ry rz
+    limits (-20.0 45.0)
+           (-30.0 30.0)
+           (-30.0 30.0)
+  end
+  begin
+     id 15 
+     name upperneck
+     direction 0.0320945 0.999136 0.0264105   
+     length 1.72601   
+     axis 0 0 0   XYZ
+    dof rx ry rz
+    limits (-20.0 45.0)
+           (-30.0 30.0)
+           (-30.0 30.0)
+  end
+  begin
+     id 16
+     name head
+     direction 0.0120552 0.999916 0.00480151  
+     length 1.79973  
+     axis 0 0 0   XYZ
+    dof rx ry rz
+    limits (-20.0 45.0)
+           (-30.0 30.0)
+           (-30.0 30.0)
+  end
+  begin
+     id 17 
+     name lclavicle
+     direction 0.909 0.371533 -0.188897  
+     length 3.47956  
+     axis 0 0 0   XYZ
+    dof ry rz
+    limits (-20.0 10.0)
+           (0.0 20.0)
+  end
+  begin
+     id 18 
+     name lhumerus
+     direction 1 -4.48963e-011 -6.48352e-028   
+     length 5.52302   
+     axis 180 -30 -90   XYZ
+    dof rx ry rz
+    limits (-60.0 90.0)
+           (-90.0 90.0)
+           (-90.0 90.0)
+  end
+  begin
+     id 19
+     name lradius
+     direction 1 -4.48968e-011 -4.78536e-027   
+     length 3.58675   
+     axis 180 -30 -90   XYZ
+    dof rx
+    limits (-10.0 170.0)
+  end
+  begin
+     id 20
+     name lwrist
+     direction 1 -4.48942e-011 -9.9435e-027   
+     length 1.79337   
+     axis 8.54312e-015 90 90   XYZ
+    dof ry
+    limits (-180.0 0.0)
+ end
+  begin
+     id 21 
+     name lhand
+     direction 1 -4.4904e-011 -1.9887e-026   
+     length 0.536412   
+     axis 1.26896e-014 90 90   XYZ
+    dof rx rz
+    limits (-90.0 90.0)
+           (-45.0 45.0)
+  end
+  begin
+     id 22 
+     name lfingers
+     direction 1 -4.48893e-011 -3.9774e-026   
+     length 0.432469   
+     axis 2.53792e-014 90 90   XYZ
+    dof rx
+    limits (0.0 90.0)
+  end
+  begin
+     id 23
+     name lthumb
+     direction 0.707107 -6.34934e-011 0.707107   
+     length 0.620942   
+     axis -90 45 2.85299e-015   XYZ
+    dof rx rz
+    limits (-45.0 45.0)
+           (-45.0 45.0)
+  end
+  begin
+     id 24 
+     name rclavicle
+     direction -0.89595 0.416672 -0.153813   
+     length 3.44099  
+     axis 0 0 0   XYZ 
+    dof ry rz
+    limits (-10.0 20.0)
+           (-20.0 0.0)
+  end
+  begin
+     id 25 
+     name rhumerus
+     direction -1 -4.48952e-011 5.57804e-028  
+     length 5.8478   
+     axis 180 30 90   XYZ
+    dof rx ry rz
+    limits (-90.0 60.0)
+           (-90.0 90.0)
+           (-90.0 90.0)
+  end
+  begin
+     id 26 
+     name rradius
+     direction -1 -4.48967e-011 -7.93646e-027   
+     length 3.62717   
+     axis 180 30 90   XYZ
+    dof rx
+    limits (-10.0 170.0)
+  end
+  begin
+     id 27 
+     name rwrist
+     direction -1 -4.49008e-011 -9.9435e-027   
+     length 1.81359   
+     axis 8.54312e-015 -90 -90   XYZ
+    dof ry
+    limits (-180.0 0.0)
+  end
+  begin
+     id 28
+     name rhand
+     direction -1 -4.48812e-011 -1.9887e-026   
+     length 0.650516   
+     axis 1.26896e-014 -90 -90   XYZ
+    dof rx rz
+    limits (-90.0 90.0)
+           (-45.0 45.0)
+  end
+  begin
+     id 29
+     name rfingers
+     direction -1 -4.49228e-011 -3.9774e-026   
+     length 0.524463   
+     axis 2.53792e-014 -90 -90   XYZ
+    dof rx
+    limits (0.0 90.0)
+  end
+  begin
+     id 30
+     name rthumb
+     direction -0.707107 -6.34912e-011 0.707107   
+     length 0.753027   
+     axis -90 -45 -2.85299e-015   XYZ
+    dof rx rz
+    limits (-45.0 45.0)
+           (-45.0 45.0)
+  end
+:hierarchy
+  begin
+    root lhipjoint rhipjoint lowerback
+    lhipjoint lfemur
+    lfemur ltibia
+    ltibia lfoot
+    lfoot ltoes
+    rhipjoint rfemur
+    rfemur rtibia
+    rtibia rfoot
+    rfoot rtoes
+    lowerback upperback
+    upperback thorax
+    thorax lowerneck lclavicle rclavicle
+    lowerneck upperneck
+    upperneck head
+    lclavicle lhumerus
+    lhumerus lradius
+    lradius lwrist
+    lwrist lhand lthumb
+    lhand lfingers
+    rclavicle rhumerus
+    rhumerus rradius
+    rradius rwrist
+    rwrist rhand rthumb
+    rhand rfingers
+  end
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+lthumb 0.53507 44.0277
+rfemur -24.5181 6.4393 21.9824
+rtibia 25.5166
+rfoot -7.52209 -15.0231
+rtoes -11.2914
+lfemur 16.8074 -2.96243 -25.4977
+ltibia 23.1411
+lfoot -15.3543 0.732768
+ltoes -20.8531
+341
+root 11.0359 17.5068 29.051 -4.01745 6.83579 0.112677
+lowerback 5.78481 -1.59455 -0.699558
+upperback 1.79665 -2.12702 1.9459
+thorax -1.29197 -1.06507 2.53672
+lowerneck -11.5009 0.460492 -1.43835
+upperneck 0.234851 0.975308 -4.41599
+head 2.07353 0.341221 -1.11418
+rclavicle 2.0773e-014 -1.74931e-014
+rhumerus -63.3308 -33.4654 -48.681
+rradius 10.8036
+rwrist 23.8476
+rhand -20.8023 12.9051
+rfingers 7.12502
+rthumb 5.56519 -17.0019
+lclavicle 2.0773e-014 -1.74931e-014
+lhumerus -30.8303 -5.16592 91.5838
+lradius 66.6221
+lwrist 27.188
+lhand -26.3312 13.7278
+lfingers 7.12502
+lthumb 0.225814 43.7075
+rfemur -24.3433 6.59562 22.6918
+rtibia 26.5286
+rfoot -6.09207 -15.7664
+rtoes -12.2351
+lfemur 16.9647 -2.79207 -24.8322
+ltibia 24.6066
+lfoot -14.9779 0.774576
+ltoes -24.8881
+342
+root 11.0323 17.4953 29.2606 -4.41668 7.13935 -0.642809
+lowerback 6.08656 -1.74843 0.1647
+upperback 1.59999 -2.27025 2.15737
+thorax -1.68055 -1.133 2.29471
+lowerneck -11.2052 0.454015 -1.6437
+upperneck 0.552324 0.997883 -4.39699
+head 2.1242 0.348976 -1.07201
+rclavicle 8.15017e-015 -1.27222e-014
+rhumerus -65.0106 -33.9481 -46.1462
+rradius 10.2501
+rwrist 25.2991
+rhand -20.8065 15.2084
+rfingers 7.12502
+rthumb 5.56121 -14.6987
+lclavicle 8.15017e-015 -1.27222e-014
+lhumerus -31.8576 -4.44121 91.7253
+lradius 66.0609
+lwrist 26.3853
+lhand -25.9912 13.1433
+lfingers 7.12502
+lthumb 0.554262 43.1223
+rfemur -23.7441 6.44938 23.6914
+rtibia 27.4619
+rfoot -5.11738 -15.758
+rtoes -5.59183
+lfemur 17.3467 -3.11875 -23.8576
+ltibia 25.8334
+lfoot -14.2605 0.0108513
+ltoes -24.2027
+343
+root 11.0237 17.502 29.4538 -4.48244 6.81263 -1.59521
+lowerback 5.7506 -1.88034 0.989992
+upperback 1.78225 -2.40255 2.42913
+thorax -1.32314 -1.20217 2.10433
+lowerneck -11.5801 0.738567 -1.03767
+upperneck 0.599575 1.32881 -4.89607
+head 2.22766 0.494526 -1.32385
+rclavicle -7.23079e-015 -1.27222e-014
+rhumerus -63.7887 -34.3173 -47.1111
+rradius 9.93192
+rwrist 23.4686
+rhand -19.5534 13.502
+rfingers 7.12502
+rthumb 6.77091 -16.3697
+lclavicle -7.23079e-015 -1.27222e-014
+lhumerus -32.0358 -3.35783 91.4085
+lradius 65.228
+lwrist 25.6386
+lhand -25.7351 12.7292
+lfingers 7.12502
+lthumb 0.801561 42.7075
+rfemur -23.2433 6.73071 25.0009
+rtibia 27.7761
+rfoot -4.35066 -15.1615
+rtoes -10.693
+lfemur 16.9276 -2.85303 -22.5749
+ltibia 27.7778
+lfoot -13.6748 -0.618019
+ltoes -24.1466
\ No newline at end of file
diff --git a/pina/egno_data/walk.asf b/pina/egno_data/walk.asf
new file mode 100644
index 00000000..852a85c3
--- /dev/null
+++ b/pina/egno_data/walk.asf
@@ -0,0 +1,338 @@
+# AST/ASF file generated using VICON BodyLanguage
+# -----------------------------------------------
+:version 1.10
+:name VICON
+:units
+  mass 1.0
+  length 0.45
+  angle deg
+:documentation
+   .ast/.asf automatically generated from VICON data using
+   VICON BodyBuilder and BodyLanguage model FoxedUp or BRILLIANT.MOD
+:root
+   order TX TY TZ RX RY RZ
+   axis XYZ
+   position 0 0 0  
+   orientation 0 0 0 
+:bonedata
+  begin
+     id 1 
+     name lhipjoint
+     direction 0.655637 -0.713449 0.247245 
+     length 2.52691 
+     axis 0 0 0  XYZ
+  end
+  begin
+     id 2 
+     name lfemur
+     direction 0.34202 -0.939693 0  
+     length 7.59371  
+     axis 0 0 20  XYZ
+    dof rx ry rz
+    limits (-160.0 20.0)
+           (-70.0 70.0)
+           (-60.0 70.0)
+  end
+  begin
+     id 3 
+     name ltibia
+     direction 0.34202 -0.939693 0   
+     length 7.28717   
+     axis 0 0 20   XYZ
+    dof rx
+    limits (-10.0 170.0)
+  end
+  begin
+     id 4 
+     name lfoot
+     direction 0.0886837 -0.243657 0.965798   
+     length 2.2218   
+     axis -90 7.62852e-016 20   XYZ
+    dof rx rz
+    limits (-45.0 90.0)
+           (-70.0 20.0)
+  end
+  begin
+     id 5 
+     name ltoes
+     direction 1.53547e-011 -4.22004e-011 1   
+     length 1.11249   
+     axis -90 7.62852e-016 20   XYZ
+    dof rx
+    limits (-90.0 20.0)
+  end
+  begin
+     id 6 
+     name rhipjoint
+     direction -0.645062 -0.722004 0.250209   
+     length 2.49697  
+     axis 0 0 0  XYZ 
+  end
+  begin
+     id 7 
+     name rfemur
+     direction -0.34202 -0.939693 0   
+     length 7.58734   
+     axis 0 0 -20   XYZ
+    dof rx ry rz
+    limits (-160.0 20.0)
+           (-70.0 70.0)
+           (-70.0 60.0)
+  end
+  begin
+     id 8 
+     name rtibia
+     direction -0.34202 -0.939693 0   
+     length 7.21538   
+     axis 0 0 -20   XYZ
+    dof rx
+    limits (-10.0 170.0)
+ end
+ begin
+     id 9 
+     name rfoot
+     direction -0.102907 -0.282735 0.953662   
+     length 2.23735  
+     axis -90 -7.62852e-016 -20   XYZ
+    dof rx rz
+    limits (-45.0 90.0)
+           (-20.0 70.0)
+  end
+  begin
+     id 10 
+     name rtoes
+     direction -1.5354e-011 -4.21751e-011 1   
+     length 1.11569   
+     axis -90 -7.62852e-016 -20   XYZ
+    dof rx
+    limits (-90.0 20.0)
+  end
+  begin
+     id 11 
+     name lowerback
+     direction 0.00952301 0.997604 -0.0685217   
+     length 2.05943   
+     axis 0 0 0   XYZ
+    dof rx ry rz
+    limits (-20.0 45.0)
+           (-30.0 30.0)
+           (-30.0 30.0)
+  end
+  begin
+     id 12 
+     name upperback
+     direction 0.00494399 0.999577 -0.0286701   
+     length 2.06523   
+     axis 0 0 0   XYZ
+    dof rx ry rz
+    limits (-20.0 45.0)
+           (-30.0 30.0)
+           (-30.0 30.0)
+  end
+  begin
+     id 13 
+     name thorax
+     direction 0.000729686 0.999992 0.00388603   
+     length 2.06807   
+     axis 0 0 0   XYZ
+    dof rx ry rz
+    limits (-20.0 45.0)
+           (-30.0 30.0)
+           (-30.0 30.0)
+  end
+  begin
+     id 14 
+     name lowerneck
+     direction 0.00452882 0.995459 0.0950816   
+     length 1.57426   
+     axis 0 0 0   XYZ
+    dof rx ry rz
+    limits (-20.0 45.0)
+           (-30.0 30.0)
+           (-30.0 30.0)
+  end
+  begin
+     id 15 
+     name upperneck
+     direction 0.021922 0.997711 -0.0639762   
+     length 1.56399   
+     axis 0 0 0   XYZ
+    dof rx ry rz
+    limits (-20.0 45.0)
+           (-30.0 30.0)
+           (-30.0 30.0)
+  end
+  begin
+     id 16
+     name head
+     direction 0.008024 0.999444 -0.0323691  
+     length 1.6265  
+     axis 0 0 0   XYZ
+    dof rx ry rz
+    limits (-20.0 45.0)
+           (-30.0 30.0)
+           (-30.0 30.0)
+  end
+  begin
+     id 17 
+     name lclavicle
+     direction 0.967826 0.247107 -0.0474446  
+     length 3.6598  
+     axis 0 0 0   XYZ
+    dof ry rz
+    limits (-20.0 10.0)
+           (0.0 20.0)
+  end
+  begin
+     id 18 
+     name lhumerus
+     direction 1 -4.48971e-011 -2.90544e-027   
+     length 4.86513   
+     axis 180 -30 -90   XYZ
+    dof rx ry rz
+    limits (-60.0 90.0)
+           (-90.0 90.0)
+           (-90.0 90.0)
+  end
+  begin
+     id 19
+     name lradius
+     direction 1 -4.48958e-011 1.79638e-026   
+     length 3.35554   
+     axis 180 -30 -90   XYZ
+    dof rx
+    limits (-10.0 170.0)
+  end
+  begin
+     id 20
+     name lwrist
+     direction 1 -4.48952e-011 3.34345e-026   
+     length 1.67777   
+     axis -2.22354e-014 90 90   XYZ
+    dof ry
+    limits (-180.0 0.0)
+ end
+  begin
+     id 21 
+     name lhand
+     direction 1 -4.49055e-011 6.6869e-026   
+     length 0.661175   
+     axis -4.26681e-014 90 90   XYZ
+    dof rx rz
+    limits (-90.0 90.0)
+           (-45.0 45.0)
+  end
+  begin
+     id 22 
+     name lfingers
+     direction 1 -4.48731e-011 1.33738e-025   
+     length 0.533057   
+     axis -8.53362e-014 90 90   XYZ
+    dof rx
+    limits (0.0 90.0)
+  end
+  begin
+     id 23
+     name lthumb
+     direction 0.707107 -6.35005e-011 0.707107   
+     length 0.765366   
+     axis -90 45 2.85299e-015   XYZ
+    dof rx rz
+    limits (-45.0 45.0)
+           (-45.0 45.0)
+  end
+  begin
+     id 24 
+     name rclavicle
+     direction -0.973174 0.211421 -0.0907389   
+     length 3.59444  
+     axis 0 0 0   XYZ 
+    dof ry rz
+    limits (-10.0 20.0)
+           (-20.0 0.0)
+  end
+  begin
+     id 25 
+     name rhumerus
+     direction -1 -4.48964e-011 1.56311e-027  
+     length 5.02649   
+     axis 180 30 90   XYZ
+    dof rx ry rz
+    limits (-90.0 60.0)
+           (-90.0 90.0)
+           (-90.0 90.0)
+  end
+  begin
+     id 26 
+     name rradius
+     direction -1 -4.48948e-011 2.01724e-026   
+     length 3.36431   
+     axis 180 30 90   XYZ
+    dof rx
+    limits (-10.0 170.0)
+  end
+  begin
+     id 27 
+     name rwrist
+     direction -1 -4.49008e-011 3.34345e-026   
+     length 1.68216   
+     axis -2.22354e-014 -90 -90   XYZ
+    dof ry
+    limits (-180.0 0.0)
+  end
+  begin
+     id 28
+     name rhand
+     direction -1 -4.49013e-011 6.6869e-026   
+     length 0.730406   
+     axis -4.26681e-014 -90 -90   XYZ
+    dof rx rz
+    limits (-90.0 90.0)
+           (-45.0 45.0)
+  end
+  begin
+     id 29
+     name rfingers
+     direction -1 -4.48867e-011 1.33738e-025   
+     length 0.588872   
+     axis -8.53362e-014 -90 -90   XYZ
+    dof rx
+    limits (0.0 90.0)
+  end
+  begin
+     id 30
+     name rthumb
+     direction -0.707107 -6.34907e-011 0.707107   
+     length 0.845506   
+     axis -90 -45 -2.85299e-015   XYZ
+    dof rx rz
+    limits (-45.0 45.0)
+           (-45.0 45.0)
+  end
+:hierarchy
+  begin
+    root lhipjoint rhipjoint lowerback
+    lhipjoint lfemur
+    lfemur ltibia
+    ltibia lfoot
+    lfoot ltoes
+    rhipjoint rfemur
+    rfemur rtibia
+    rtibia rfoot
+    rfoot rtoes
+    lowerback upperback
+    upperback thorax
+    thorax lowerneck lclavicle rclavicle
+    lowerneck upperneck
+    upperneck head
+    lclavicle lhumerus
+    lhumerus lradius
+    lradius lwrist
+    lwrist lhand lthumb
+    lhand lfingers
+    rclavicle rhumerus
+    rhumerus rradius
+    rradius rwrist
+    rwrist rhand rthumb
+    rhand rfingers
+  end
diff --git a/pina/model/block/egno_block.py b/pina/model/block/egno_block.py
new file mode 100644
index 00000000..9f04a874
--- /dev/null
+++ b/pina/model/block/egno_block.py
@@ -0,0 +1,212 @@
+import torch
+import torch.nn as nn
+import math
+
+from .message_passing.egnn_with_velo import EnEquivariantNetworkBlock
+from .temporal_convolution_layer import TemporalConvolutionLayer
+
+
+def timestep_embedding(n_timesteps, embedding_dim):
+    """
+    Implementation of sinusodial timestep embedding following
+    Xu, Han, et al. 2024 and Vaswani et al. 2017.
+
+    :param int n_timesteps: The number of timesteps solving for.
+    :param int embedding_dim: The dimension of time embedding vector.
+    """
+    if embedding_dim % 2 == 1:
+        embedding_dim -= 1
+
+    half_dim = embedding_dim // 2
+    exp = torch.arange(half_dim) / embedding_dim * 2
+    denom = 10000**exp
+
+    I_tensor = torch.arange(n_timesteps).unsqueeze(dim=1)
+    I_tensor = I_tensor.repeat(1, half_dim)
+
+    input_term = I_tensor / denom
+    sines = input_term.sin()
+    cosines = input_term.cos()
+    output = torch.cat((sines, cosines), dim=-1)
+
+    permutation = torch.zeros(embedding_dim, dtype=torch.long)
+
+    permutation[::2] = torch.arange(half_dim)
+    permutation[1::2] = torch.arange(half_dim, embedding_dim)
+
+    return output[:, permutation]
+
+
+class EquivariantGraphNeuralOperatorBlock(nn.Module):
+    """
+    Implementation of the Equivariant Graph Neural Operator.
+    This is used to model graphs that evolve with time, following Xu, Han, et al.'s method in Equivariant Graph
+    Neural Operator for Modeling 3D Dynamics in 2024.
+
+    Graphs are duplicated to learn a function to function mapping to learn
+    dynamics rather than specific functions. The layer's ensurance equivariance for positions
+    while finding this mapping through equivariant activation functions and equivariant
+    operations for weights.
+
+    .. seealso::
+
+        @misc{xu2024equivariantgraphneuraloperator,
+      title={Equivariant Graph Neural Operator for Modeling 3D Dynamics},
+      author={Minkai Xu and Jiaqi Han and Aaron Lou and Jean Kossaifi and Arvind Ramanathan and Kamyar Azizzadenesheli and Jure Leskovec and Stefano Ermon and Anima Anandkumar},
+      year={2024},
+      eprint={2401.11037},
+      archivePrefix={arXiv},
+      primaryClass={cs.LG},
+      url={https://arxiv.org/abs/2401.11037},
+        }
+    """
+
+    def __init__(
+        self,
+        n_layers,
+        time_discretizations,
+        num_scalar_features,
+        num_vec_features,
+        num_edge_features,
+        embedding_dim,
+        n_nodes,
+        n_modes,
+        eq_activation,
+        scalar_feature_activation,
+    ):
+        """
+        Initialization of the :class:'EquivariantGraphNeuralOperatorBlock' class.
+
+        :param int n_layer: Number of temporal convolution layers
+        :param time_discretizations: Number of timesteps solving for.
+        :param int num_scalar_features: The number of scalar features per node.
+        :param int num_vec_features: The number of vector features per node.
+        :param int num_edge_features: The number of edge features.
+        :param int embedding_dim: The dimension of the time embedding vectors.
+        :param int n_nodes: The number of nodes in the graph.
+        :param int n_modes: The number of modes kept after fft.
+        :param eq_activation: The activation function for vector featues.
+        :param scalar_feature_activation: The activation function for the scalar features.
+        """
+        super().__init__()
+
+        # f_h becomes the concatenation of scalar features and embedding vectors
+        f_h_size = num_scalar_features + embedding_dim
+        f_z_size = num_vec_features
+
+        self.layers = nn.ModuleList()
+
+        for _ in range(n_layers):
+            self.layers.append(
+                TemporalConvolutionLayer(
+                    time_discretizations,
+                    n_nodes,
+                    f_h_size,
+                    f_z_size,
+                    n_modes,
+                    eq_activation,
+                    scalar_feature_activation,
+                )
+            )
+
+        self._time_discretizations = time_discretizations
+        self._n_nodes = n_nodes
+        self._embedding_dim = embedding_dim
+
+        self.egnn_layer = EnEquivariantNetworkBlock(
+            f_h_size, num_edge_features, f_z_size, True
+        )
+
+    def forward(
+        self, features, positions, edge_indices, edge_attributes, velos
+    ):
+        """
+        Forward Pass of the block, computing the sequential graphs of the input
+
+        :param features: The node features.
+        :type features: torch.Tensor | [Nodes, H]
+        :param positions: The node positions.
+        :type positions: torch.Tensor | [Nodes, 3]
+        :param edge_indicies: The connections between nodes.
+        :type edge_indicies: torch.Tensor | [2, Edges]
+        :param edge_attributes: The attributes of each edge.
+        :type edge_attributes: torch.Tensor | [Edges, Features]
+        :return The updated node features and node positions.
+        :rtype: tuple(torch.Tensor, torch.Tensor)
+        """
+
+        # Adjust edge index and edge attribute tensors to handle the duplication of graphs
+        time_index_jumps = (
+            torch.arange(0, self._time_discretizations) * self._n_nodes
+        )
+        per_node_index_jumps = time_index_jumps.repeat_interleave(
+            edge_indices.shape[1], dim=0
+        )
+        outgoing_edges = (
+            edge_indices[0].repeat(self._time_discretizations)
+            + per_node_index_jumps
+        )
+        incoming_edges = (
+            edge_indices[1].repeat(self._time_discretizations)
+            + per_node_index_jumps
+        )
+        edge_indices = torch.stack([outgoing_edges, incoming_edges])
+
+        # Repeating edge attributes for each time discretization
+        edge_attributes = edge_attributes.repeat(self._time_discretizations, 1)
+
+        # time discretize
+        # Arguments -> [Time_discretizations, embedding_dim]
+        time_embds = timestep_embedding(
+            self._time_discretizations, self._embedding_dim
+        )
+
+        # Repeats the timeembedding for each node
+        # [Time_discretizations, embedding_dim] -> [Time_discretizations, Nodes, embedding_dim]
+        time_embds = time_embds.unsqueeze(1).repeat(1, self._n_nodes, 1)
+
+        if features is None:
+            features = time_embds
+        else:
+            features = features.repeat(self._time_discretizations, 1, 1)
+
+            # Features with time added
+            # -> [Time_descretizations, Nodes, features + embedding_dim]
+            features = torch.cat((features, time_embds), dim=2)
+
+        # -> Mean: [1,Coords]
+        mean = positions.mean(dim=0, keepdim=True)
+
+        # [Nodes, Coords] -> [Time_discretizations, Nodes, Coords]
+        positions = positions.repeat(self._time_discretizations, 1, 1)
+        # [Nodes, Vector] -> [Time_discretizations, Nodes, Vector]
+        velos = velos.repeat(self._time_discretizations, 1, 1)
+
+        for layer in self.layers:
+            positions = positions - mean
+
+            vecfeatures = torch.stack((positions, velos), dim=2)
+
+            features, vecfeatures = layer(features, vecfeatures)
+            positions = vecfeatures[..., 0, :]
+            positions = positions + mean
+
+            velos = vecfeatures[..., 1, :]
+
+        # Apply EGNN layer (input: [Time*Nodes, Feature/Coords])
+        features, positions = self.egnn_layer(
+            features.reshape(self._time_discretizations * self._n_nodes, -1),
+            positions.reshape(self._time_discretizations * self._n_nodes, -1),
+            edge_indices,
+            edge_attributes,
+            velos.reshape(self._time_discretizations * self._n_nodes, -1),
+        )
+
+        features = features.view(self._time_discretizations, self._n_nodes, -1)
+        positions = positions.view(
+            self._time_discretizations, self._n_nodes, -1
+        )
+
+        # features: [Time discr, Nodes, Features]
+        # positions: [Time, Nodes, Coords]
+        return features, positions
diff --git a/pina/model/block/message_passing/egnn_with_velo.py b/pina/model/block/message_passing/egnn_with_velo.py
new file mode 100644
index 00000000..9e66010a
--- /dev/null
+++ b/pina/model/block/message_passing/egnn_with_velo.py
@@ -0,0 +1,256 @@
+"""Module for the E(n) Equivariant Graph Neural Network block."""
+
+import torch
+from torch_geometric.nn import MessagePassing
+from torch_geometric.utils import degree
+from ....utils import check_positive_integer
+from ....model import FeedForward
+
+
+class EnEquivariantNetworkBlock(MessagePassing):
+    """
+    Implementation of the E(n) Equivariant Graph Neural Network block.
+    This block is used to perform message-passing between nodes and edges in a
+    graph neural network, following the scheme proposed by Satorras et al. in
+    2021. It serves as an inner block in a larger graph neural network
+    architecture.
+
+    The message between two nodes connected by an edge is computed by applying a
+    linear transformation to the sender node features and the edge features,
+    together with the squared euclidean distance between the sender and
+    recipient node positions, followed by a non-linear activation function.
+    Messages are then aggregated using an aggregation scheme (e.g., sum, mean,
+    min, max, or product).
+
+    The update step is performed by applying another MLP to the concatenation of
+    the incoming messages and the node features. Here, also the node
+    positions are updated by adding the incoming messages divided by the
+    degree of the recipient node.
+
+    .. seealso::
+
+        **Original reference** Satorras, V. G., Hoogeboom, E., Welling, M.
+        (2021). *E(n) Equivariant Graph Neural Networks.*
+        In International Conference on Machine Learning.
+        DOI: `<https://doi.org/10.48550/arXiv.2102.09844>`_.
+    """
+
+    def __init__(
+        self,
+        node_feature_dim,
+        edge_feature_dim,
+        pos_dim,
+        has_velo=False,
+        hidden_dim=64,
+        n_message_layers=2,
+        n_update_layers=2,
+        activation=torch.nn.SiLU,
+        aggr="add",
+        node_dim=-2,
+        flow="source_to_target",
+
+    ):
+        """
+        Initialization of the :class:`EnEquivariantNetworkBlock` class.
+
+        :param int node_feature_dim: The dimension of the node features.
+        :param int edge_feature_dim: The dimension of the edge features.
+        :param int pos_dim: The dimension of the position features.
+        :param int hidden_dim: The dimension of the hidden features.
+            Default is 64.
+        :param int n_message_layers: The number of layers in the message
+            network. Default is 2.
+        :param int n_update_layers: The number of layers in the update network.
+            Default is 2.
+        :param torch.nn.Module activation: The activation function.
+            Default is :class:`torch.nn.SiLU`.
+        :param str aggr: The aggregation scheme to use for message passing.
+            Available options are "add", "mean", "min", "max", "mul".
+            See :class:`torch_geometric.nn.MessagePassing` for more details.
+            Default is "add".
+        :param int node_dim: The axis along which to propagate. Default is -2.
+        :param str flow: The direction of message passing. Available options
+            are "source_to_target" and "target_to_source".
+            The "source_to_target" flow means that messages are sent from
+            the source node to the target node, while the "target_to_source"
+            flow means that messages are sent from the target node to the
+            source node. See :class:`torch_geometric.nn.MessagePassing` for more
+            details. Default is "source_to_target".
+        :raises AssertionError: If `node_feature_dim` is not a positive integer.
+        :raises AssertionError: If `edge_feature_dim` is a negative integer.
+        :raises AssertionError: If `pos_dim` is not a positive integer.
+        :raises AssertionError: If `hidden_dim` is not a positive integer.
+        :raises AssertionError: If `n_message_layers` is not a positive integer.
+        :raises AssertionError: If `n_update_layers` is not a positive integer.
+        """
+        super().__init__(aggr=aggr, node_dim=node_dim, flow=flow)
+
+        # Check values
+        check_positive_integer(node_feature_dim, strict=True)
+        check_positive_integer(edge_feature_dim, strict=False)
+        check_positive_integer(pos_dim, strict=True)
+        check_positive_integer(hidden_dim, strict=True)
+        check_positive_integer(n_message_layers, strict=True)
+        check_positive_integer(n_update_layers, strict=True)
+
+        # Layer for computing the message
+        self.message_net = FeedForward(
+            input_dimensions=2 * node_feature_dim + edge_feature_dim + 1,
+            output_dimensions=pos_dim,
+            inner_size=hidden_dim,
+            n_layers=n_message_layers,
+            func=activation,
+        )
+
+        # Layer for updating the node features
+        self.update_feat_net = FeedForward(
+            input_dimensions=node_feature_dim + pos_dim,
+            output_dimensions=node_feature_dim,
+            inner_size=hidden_dim,
+            n_layers=n_update_layers,
+            func=activation,
+        )
+
+        # Layer for updating the node positions
+        # The output dimension is set to 1 for equivariant updates
+        self.update_pos_net = FeedForward(
+            input_dimensions=pos_dim,
+            output_dimensions=1,
+            inner_size=hidden_dim,
+            n_layers=n_update_layers,
+            func=activation,
+        )
+
+        if has_velo:
+            self.update_velo_net = FeedForward(
+                input_dimensions=node_feature_dim,
+                output_dimensions=1,
+                inner_size=hidden_dim,
+                n_layers=n_update_layers,
+                func=activation,
+            )
+
+
+
+    def forward(self, x, pos, edge_index, edge_attr=None, velo=None):
+        """
+        Forward pass of the block, triggering the message-passing routine.
+
+        :param x: The node features.
+        :type x: torch.Tensor | LabelTensor
+        :param pos: The euclidean coordinates of the nodes.
+        :type pos: torch.Tensor | LabelTensor
+        :param torch.Tensor edge_index: The edge indices.
+        :param edge_attr: The edge attributes. Default is None.
+        :type edge_attr: torch.Tensor | LabelTensor
+        :return: The updated node features and node positions.
+        :rtype: tuple(torch.Tensor, torch.Tensor)
+        """
+        size = (x.shape[0], x.shape[0])
+        if velo is not None:
+            return self.propagate(
+            edge_index=edge_index, size=size, x=x, pos=pos, edge_attr=edge_attr, velo=velo
+        )
+
+        return self.propagate(
+            edge_index=edge_index, size=size, x=x, pos=pos, edge_attr=edge_attr
+        )
+
+    def message(self, x_i, x_j, pos_i, pos_j, edge_attr):
+        """
+        Compute the message to be passed between nodes and edges.
+
+        :param x_i: The node features of the recipient nodes.
+        :type x_i: torch.Tensor | LabelTensor
+        :param x_j: The node features of the sender nodes.
+        :type x_j: torch.Tensor | LabelTensor
+        :param pos_i: The node coordinates of the recipient nodes.
+        :type pos_i: torch.Tensor | LabelTensor
+        :param pos_j: The node coordinates of the sender nodes.
+        :type pos_j: torch.Tensor | LabelTensor
+        :param edge_attr: The edge attributes.
+        :type edge_attr: torch.Tensor | LabelTensor
+        :return: The message to be passed.
+        :rtype: tuple(torch.Tensor, torch.Tensor)
+        """
+        # Compute the euclidean distance between the sender and recipient nodes
+        diff = pos_i - pos_j
+        dist = torch.norm(diff, dim=-1, keepdim=True) ** 2
+  
+        # Compute the message input
+        if edge_attr is None:
+            input_ = torch.cat((x_i, x_j, dist), dim=-1)
+        else:
+            
+            input_ = torch.cat((x_i, x_j, dist, edge_attr), dim=-1)
+
+        # Compute the messages and their equivariant counterpart
+        m_ij = self.message_net(input_)
+        message = diff * self.update_pos_net(m_ij)
+
+        return message, m_ij
+
+    def aggregate(self, inputs, index, ptr=None, dim_size=None):
+        """
+        Aggregate the messages at the nodes during message passing.
+
+        This method receives a tuple of tensors corresponding to the messages
+        to be aggregated. Both messages are aggregated separately according to
+        the specified aggregation scheme.
+
+        :param tuple(torch.Tensor) inputs: Tuple containing two messages to
+            aggregate.
+        :param index: The indices of target nodes for each message. This tensor
+            specifies which node each message is aggregated into.
+        :type index: torch.Tensor | LabelTensor
+        :param ptr: Optional tensor to specify the slices of messages for each
+            node (used in some aggregation strategies). Default is None.
+        :type ptr: torch.Tensor | LabelTensor
+        :param int dim_size: Optional size of the output dimension, i.e.,
+            number of nodes. Default is None.
+        :return: Tuple of aggregated tensors corresponding to (aggregated
+            messages for position updates, aggregated messages for feature
+            updates).
+        :rtype: tuple(torch.Tensor, torch.Tensor)
+        """
+        # Unpack the messages from the inputs
+        message, m_ij = inputs
+
+        # Aggregate messages as usual using self.aggr method
+        agg_message = super().aggregate(message, index, ptr, dim_size)
+        agg_m_ij = super().aggregate(m_ij, index, ptr, dim_size)
+
+        return agg_message, agg_m_ij
+
+    def update(self, aggregated_inputs, x, pos, edge_index, velo=None):
+        """
+        Update the node features and the node coordinates with the received
+        messages.
+
+        :param tuple(torch.Tensor) aggregated_inputs: The messages to be passed.
+        :param x: The node features.
+        :type x: torch.Tensor | LabelTensor
+        :param pos: The euclidean coordinates of the nodes.
+        :type pos: torch.Tensor | LabelTensor
+        :param torch.Tensor edge_index: The edge indices.
+        :return: The updated node features and node positions.
+        :rtype: tuple(torch.Tensor, torch.Tensor)
+        """
+        # aggregated_inputs is tuple (agg_message, agg_m_ij)
+        agg_message, agg_m_ij = aggregated_inputs
+
+        # Update node features with aggregated m_ij
+        x = self.update_feat_net(torch.cat((x, agg_m_ij), dim=-1))
+        
+        # Degree for normalization of position updates
+        c = degree(edge_index[1], pos.shape[0]).unsqueeze(-1).clamp(min=1)
+
+        if velo is not None:
+            # Velo net
+            # the multplication broads casts to a element wise multiplication
+            v = self.update_velo_net(x) * velo + agg_message/c
+            pos = pos + v
+        else:
+            pos = pos + agg_message / c
+
+        return x, pos
\ No newline at end of file
diff --git a/pina/model/block/temporal_convolution_layer.py b/pina/model/block/temporal_convolution_layer.py
new file mode 100644
index 00000000..89c6f37a
--- /dev/null
+++ b/pina/model/block/temporal_convolution_layer.py
@@ -0,0 +1,197 @@
+import torch
+from torch import nn
+from ...utils import check_consistency
+
+
+class M_H(nn.Module):
+    """
+    Implementation of non equivariant weight application for scalar features.
+    """
+
+    def __init__(self, n_scalar_features, num_modes):
+        super().__init__()
+
+        scale = 1.0 / (n_scalar_features**2)
+        self.weights = nn.Parameter(
+            scale
+            * torch.rand(
+                num_modes,
+                n_scalar_features,
+                n_scalar_features,
+                dtype=torch.cfloat,
+            )
+        )
+
+    def forward(self, f_h):
+        """
+        Forward Pass of weights onto scalar features.
+
+        :param f_h: The scalar node features.
+        :type f_h: torch.Tensor | [Nodes, n_scalar_features]
+        """
+        num_modes = self.weights.shape[0]
+
+        fft_f_h = torch.fft.fft(f_h, dim=0)
+        fft_f_h = fft_f_h[:num_modes, ...]
+
+        M_h_dot_f_h = torch.einsum("ijl, inl -> inj", self.weights, fft_f_h)
+
+        full = torch.zeros_like(f_h)
+        full[:num_modes, ...] = M_h_dot_f_h
+
+        return torch.fft.ifft(full)
+
+
+class M_Z(nn.Module):
+    """
+    Implementation of equivariant weight application for vector features.
+    """
+
+    def __init__(self, n_vec_features, num_modes):
+        super().__init__()
+
+        scale = 1.0 / (n_vec_features**2)
+        self.weights = nn.Parameter(
+            scale
+            * torch.rand(
+                num_modes, n_vec_features, n_vec_features, dtype=torch.cfloat
+            )
+        )
+
+    def forward(self, f_z):
+        """
+        Forward Pass of weights onto vector features.
+
+        :param f_h: The vector node features.
+        :type f_h: torch.Tensor | [Nodes, n_vec_features]
+        """
+        num_modes = self.weights.shape[0]
+
+        fft_f_z = torch.fft.fft(f_z, dim=0)
+        fft_f_z = fft_f_z[:num_modes, ...]
+
+        M_z_dot_f_z = torch.einsum("isl, inlT -> insT", self.weights, fft_f_z)
+
+        full = torch.zeros_like(f_z)
+        full[:num_modes, ...] = M_z_dot_f_z
+
+        return torch.fft.ifft(full)
+
+
+class TemporalConvolutionLayer(nn.Module):
+    """
+    Implementation of the Temporal Convolution Layer for Equivariant Graph Neural Operators.
+    This block ensures equivariance of 3D vector features while accurately calculating
+    scalar features, following Xu, Han, et al.'s method in Equivariant Graph
+    Neural Operator for Modeling 3D Dynamics in 2024.
+
+    Equivariance is ensured through vector-scalar multiplication of vector featues
+    and weight matricies as well as equivariant activation functions.
+
+    .. seealso::
+
+        @misc{xu2024equivariantgraphneuraloperator,
+      title={Equivariant Graph Neural Operator for Modeling 3D Dynamics},
+      author={Minkai Xu and Jiaqi Han and Aaron Lou and Jean Kossaifi and Arvind Ramanathan and Kamyar Azizzadenesheli and Jure Leskovec and Stefano Ermon and Anima Anandkumar},
+      year={2024},
+      eprint={2401.11037},
+      archivePrefix={arXiv},
+      primaryClass={cs.LG},
+      url={https://arxiv.org/abs/2401.11037},
+        }
+    """
+
+    def __init__(
+        self,
+        time_discretizations,
+        n_nodes,
+        f_h_size,
+        f_z_size,
+        n_modes,
+        eq_activation,
+        scalar_feature_activation,
+    ):
+        """
+        Initialization of the :class: 'TemporalConvolutionLayer' class
+
+        :param: int time_discretizations: Number of timesteps solving for.
+        :param: int n_nodes: The number of nodes in graph.
+        :param: int f_h_size: The number of scalar features.
+        :param: int f_z_size: The number of 3D vector features.
+        :param: int n_modes: The number of modes kept after fft.
+        :param: eq_activation: The equivariant activation function for vector features.
+        :param: activation_scalar_featues: The activation function for scalar features.
+        """
+
+        super().__init__()
+
+        # Check type consistency
+        check_consistency(n_nodes, int)
+        check_consistency(f_h_size, int)
+        check_consistency(f_z_size, int)
+        check_consistency(time_discretizations, int)
+
+        # assign variables
+        self._f_h_size = f_h_size
+        self._f_z_size = f_z_size
+
+        self.M_H = M_H(f_h_size, n_modes)
+        self.M_Z = M_Z(f_z_size, n_modes)
+
+        self.eq_activation = eq_activation
+        self.scalar_feature_activation = scalar_feature_activation
+
+    def forward(self, x):
+        """
+        Forward Pass of the temporal convolution layer, transforming both the
+        scalar and vector features by learnable weights.
+
+        :param x: The node scalar features stacked upon node vector features.
+        :type x: torch.Tensor | [Nodes, n_scalar_features + 3 * n_vec_features]
+        """
+        f_h, f_z = x.split([self._f_h_size, 3 * self._f_z_size], dim=-1)
+
+        time_discretizations = self.M_H.shape[0]
+        n_nodes = self.M_H.shape[1]
+
+        f_z = f_z.reshape(
+            time_discretizations,
+            n_nodes,
+            self._f_z_size,
+            3,
+        )
+
+        # apply weights
+        out_f_h = self.M_H(f_h)
+        out_f_z = self.M_Z(f_z)
+
+        # reshape to remove compress coordinate dimension
+        out_f_z = f_z.reshape(time_discretizations, n_nodes, -1)
+
+        out_f_h = self.scalar_feature_activation(out_f_h)
+        out_f_z = self.eq_activation(out_f_z)
+
+        return x + torch.concat([out_f_h, out_f_z], dim=-1)
+
+    def forward(self, f_h, f_z):
+        """
+        Forward Pass of the temporal convolution layer, transforming both the
+        scalar and vector features by learnable weights.
+
+        :param f_h: The node scalar features.
+        :type f_h: torch.Tensor | [Nodes, n_scalar_features]
+        :param f_z: The node vector features.
+        :type f_z: torch.Tensor | [Nodes, n_vec_features]
+        """
+
+        # apply weights
+        out_f_h = self.M_H(f_h)
+        out_f_z = self.M_Z(f_z)
+
+        out_f_h = out_f_h.real
+        out_f_z = out_f_z.real
+
+        out_f_h = self.scalar_feature_activation(out_f_h)
+        out_f_z = self.eq_activation(out_f_z)
+
+        return f_h + out_f_h, f_z + out_f_z
diff --git a/pina/model/egnn.py b/pina/model/egnn.py
new file mode 100644
index 00000000..3e3645b8
--- /dev/null
+++ b/pina/model/egnn.py
@@ -0,0 +1,27 @@
+import torch
+import torch.nn as nn
+
+class EquivariantGraphNeuralNetwork(nn.Module):
+    def __init__(self,
+        node_feature_dim,
+        edge_feature_dim,
+        pos_dim,
+        n_egnn_layers = 2,
+        has_velo=False,
+        hidden_dim=64,
+        n_message_layers=2,
+        n_update_layers=2,
+        activation=torch.nn.SiLU,
+        aggr="add",
+        node_dim=-2,
+        flow="source_to_target"
+    ):
+        super().__init__()
+
+        self.layers - nn.ModuleList()
+        self.n_egnn_layers = n_egnn_layers
+        self.has_velo = has_velo
+
+        
+
+
diff --git a/pina/model/egno.py b/pina/model/egno.py
new file mode 100644
index 00000000..40b5e1a0
--- /dev/null
+++ b/pina/model/egno.py
@@ -0,0 +1,3 @@
+import torch
+import torch.nn as nn
+from .block.egno_block import EquivariantGraphNeuralOperatorBlock
\ No newline at end of file