DOC how to prevent dictionary unpacking in forward (#941)

skorch automatically unpacks dictionaries when passing input arguments
to the module's forward method. Sometimes, this may not be wanted. This
PR updates the docs to show how to prevent that.

docs/user/dataset.rst

@@ -91,7 +91,7 @@ In addition to the types above, you can pass dictionaries or lists of
 one of those data types, e.g. a dictionary of
 :class:`numpy.ndarray`\s. When you pass dictionaries, the keys of the
 dictionaries are used as the argument name for the
-:func:`~torch.nn.Module.forward` method of the net's
+:meth:`~torch.nn.Module.forward` method of the net's
 ``module``. Similarly, the column names of pandas ``DataFrame``\s are
 used as argument names. The example below should illustrate how to use
 this feature:
@@ -128,7 +128,7 @@ this feature:
     net.fit(X, y)
 
 Note that the keys in the dictionary ``X`` exactly match the argument
-names in the :func:`~torch.nn.Module.forward` method. This way, you
+names in the :meth:`~torch.nn.Module.forward` method. This way, you
 can easily work with several different types of input features.
 
 The :class:`.Dataset` from skorch makes the assumption that you always
@@ -144,3 +144,24 @@ apply your own transformation on the data, you should subclass
 :class:`.Dataset` and override the
 :func:`~skorch.dataset.Dataset.transform` method, then pass your
 custom class to :class:`.NeuralNet` as the ``dataset`` argument.
+
+Preventing dictionary unpacking
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+As noted, when ``X`` is a dictionary, it is automatically unpacked when passed
+to the module's :meth:`~torch.nn.Module.forward` method. Sometimes, you may want
+to prevent this, e.g. because you're using a ``module`` from another library
+that expects a dict as input, or because the exact dict keys are unknown. This
+can be achieved by wrapping the original ``module`` and packing the dict again:
+
+.. code:: python
+
+    from other_lib import ModuleExpectingDict
+
+    class WrappedModule(ModuleExpectingDict):
+        def forward(self, **kwargs):
+            # catch **kwargs, pass as a dict
+            return super().forward(kwargs)
+
+Similarly, wrapping the ``module`` can be used to make any other desired changes
+to the input arguments.