DOC: Corrections about passing different array API classes to methods

doc/sources/array_api.rst

@@ -68,11 +68,16 @@ in many cases they are.
 
 .. warning::
     If array API inputs are passed to an estimator's ``.fit()``, subsequent data passed to methods such as
-    ``.predict()`` or ``.score()`` of the fitted model might be of a different class than the ``X``/``y`` passed to
-    ``.fit()``, but **it must reside on the same device** - meaning: a model that was fitted with GPU arrays cannot
-    make predictions on CPU arrays, and a model fitted with CPU array API inputs cannot make predictions on GPU
-    arrays, even if they are of the same class. Attempting to pass data on the wrong device might lead to
-    process-wide crashes.
+    ``.predict()`` or ``.score()`` of the fitted model **must reside on the same device** - meaning: a model that
+    was fitted with GPU arrays cannot make predictions on CPU arrays, and a model fitted with CPU array API inputs
+    cannot make predictions on GPU arrays, even if they are of the same class. Attempting to pass data on the
+    wrong device might lead to process-wide crashes.
+
+.. note::
+    An estimator fitted to array API inputs should only be passed objects of the same class that was passed to
+    ``.fit()`` in subsequent calls to ``.predict()``, ``.score()``, and similar. In some cases, it might be
+    possible to pass a different class at prediction time without errors (particularly when fitting on CPU only),
+    but this is generally not supported and users should not rely on these interchanges working reliably.
 
 .. note::
     The ``target_offload`` option in config contexts and settings is not intended to work with array API
@@ -145,20 +150,6 @@ GPU operations on GPU arrays
                pred = model.predict(X[:5])
            assert isinstance(pred, torch.Tensor)
 
-           # Fitted models can be passed array API inputs of a different class
-           # than the training data, as long as their data resides in the same
-           # device. This now fits a model using a non-NumPy class whose data is on CPU.
-           X_cpu = torch.tensor(X_np, device="cpu")
-           y_cpu = torch.tensor(y_np, device="cpu")
-           model_cpu = LinearRegression()
-           with config_context(array_api_dispatch=True):
-               model_cpu.fit(X_cpu, y_cpu)
-               pred_torch = model_cpu.predict(X_cpu[:5])
-               pred_np = model_cpu.predict(X_cpu[:5].numpy())
-           assert isinstance(pred_torch, X_cpu.__class__)
-           assert isinstance(pred_np, np.ndarray)
-           assert pred_torch.__class__ != pred_np.__class__
-
     .. tab:: With DPNP arrays
        .. code-block:: python
 
@@ -193,20 +184,6 @@ GPU operations on GPU arrays
                pred = model.predict(X[:5])
            assert isinstance(pred, X.__class__)
 
-           # Fitted models can be passed array API inputs of a different class
-           # than the training data, as long as their data resides in the same
-           # device. This now fits a model using a non-NumPy class whose data is on CPU.
-           X_cpu = dpnp.array(X_np, device="cpu")
-           y_cpu = dpnp.array(y_np, device="cpu")
-           model_cpu = LinearRegression()
-           with config_context(array_api_dispatch=True):
-               model_cpu.fit(X_cpu, y_cpu)
-               pred_dpnp = model_cpu.predict(X_cpu[:5])
-               pred_np = model_cpu.predict(X_cpu[:5].asnumpy())
-           assert isinstance(pred_dpnp, X_cpu.__class__)
-           assert isinstance(pred_np, np.ndarray)
-           assert pred_dpnp.__class__ != pred_np.__class__
-
 
 ``array-api-strict``
 --------------------