def partial_fit(self, X, y=None, classes=None, **fit_params):
        """Fit the module.

        If the module is initialized, it is not re-initialized, which
        means that this method should be used if you want to continue
        training a model (warm start).
        If possible, signal-related parameters are inferred from the
        data and passed to the module at initialisation.
        Depending on the type of input passed, the following parameters
        are inferred:

          * mne.Epochs: ``n_times``, ``n_chans``, ``n_outputs``, ``chs_info``,
            ``sfreq``, ``input_window_seconds``
          * array-like: ``n_times``, ``n_chans``, ``n_outputs``
          * WindowsDataset with ``targets_from='metadata'``
            (or BaseConcatDataset of such datasets): ``n_times``, ``n_chans``, ``n_outputs``
          * other Dataset: ``n_times``, ``n_chans``
          * other types: no parameters are inferred.

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

            * mne.Epochs
            * 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.

        classes : array, sahpe (n_classes,)
          Solely for sklearn compatibility, currently unused.

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

        """
        # this needs to be executed before the net is initialized:
        if not self.signal_args_set_:
            self._set_signal_args(X, y, classes)
            self.signal_args_set_ = True
        return super().partial_fit(X=X, y=y, classes=classes, **fit_params)
Fit the module.

        If the module is initialized, it is not re-initialized, which
        means that this method should be used if you want to continue
        training a model (warm start).
        If possible, signal-related parameters are inferred from the
        data and passed to the module at initialisation.
        Depending on the type of input passed, the following parameters
        are inferred:

          * mne.Epochs: ``n_times``, ``n_chans``, ``n_outputs``, ``chs_info``,
            ``sfreq``, ``input_window_seconds``
          * array-like: ``n_times``, ``n_chans``, ``n_outputs``
          * WindowsDataset with ``targets_from='metadata'``
            (or BaseConcatDataset of such datasets): ``n_times``, ``n_chans``, ``n_outputs``
          * other Dataset: ``n_times``, ``n_chans``
          * other types: no parameters are inferred.

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

            * mne.Epochs
            * 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.

        classes : array, sahpe (n_classes,)
          Solely for sklearn compatibility, currently unused.

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

        
braindecode/eegneuralnet.py