def fit(self, X, y=None, **fit_params):
        """Initialize and fit the module.

        If the module was already initialized, by calling fit, the
        module will be re-initialized (unless ``warm_start`` is True).

        Parameters
        ----------
        X : input data, compatible with skorch.dataset.Dataset
          By default, you should be able to pass:

            * numpy arrays
            * torch tensors
            * pandas DataFrame or Series
            * scipy sparse CSR matrices
            * a dictionary of the former three
            * a list/tuple of the former three
            * a Dataset

          If this doesn't work with your data, you have to pass a
          ``Dataset`` that can deal with the data.

        y : target data, compatible with skorch.dataset.Dataset
          The same data types as for ``X`` are supported. If your X is
          a Dataset that contains the target, ``y`` may be set to
          None.

        **fit_params : dict
          Additional parameters passed to the ``forward`` method of
          the module and to the ``self.train_split`` call.

        """
        if not self.warm_start or not self.initialized_:
            self.initialize()

        self.partial_fit(X, y, **fit_params)
        return self