How to use the skorch.utils.is_dataset function in skorch

def check_data(self, X, y):
        if (
                (y is None) and
                (not is_dataset(X)) and
                (self.iterator_train is DataLoader)
        ):
            raise ValueError("No y-values are given (y=None). You must "
                             "implement your own DataLoader for training "
                             "(and your validation) and supply it using the "
                             "``iterator_train`` and ``iterator_valid`` "
                             "parameters respectively.")
        if y is None:
            # The user implements its own mechanism for generating y.
            return

        if get_dim(y) == 1:
            msg = (
                "The target data shouldn't be 1-dimensional but instead have "
                "2 dimensions, with the second dimension having the same size "
                "as the number of regression targets (usually 1). Please "

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.

        Returns
        -------
        dataset
          The initialized dataset.

        """
        if is_dataset(X):
            return X

        dataset = self.dataset
        is_initialized = not callable(dataset)

        kwargs = self._get_params_for('dataset')
        if kwargs and is_initialized:
            raise TypeError("Trying to pass an initialized Dataset while "
                            "passing Dataset arguments ({}) is not "
                            "allowed.".format(kwargs))

        if is_initialized:
            return dataset

        return dataset(X, y, **kwargs)

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.

        Returns
        -------
        dataset
          The initialized dataset.

        """
        if is_dataset(X):
            return X

        dataset = self.dataset
        is_initialized = not callable(dataset)

        kwargs = self._get_params_for('dataset')
        if kwargs and is_initialized:
            raise TypeError("Trying to pass an initialized Dataset while "
                            "passing Dataset arguments ({}) is not "
                            "allowed.".format(kwargs))

        if is_initialized:
            return dataset

        return dataset(X, y, **kwargs)

Whether to set the module to train mode or not.

        device : string (default='cpu')
          The device to store each inference result on.
          This defaults to CPU memory since there is genereally
          more memory available there. For performance reasons
          this might be changed to a specific CUDA device,
          e.g. 'cuda:0'.

        Yields
        ------
        yp : torch tensor
          Result from a forward call on an individual batch.

        """
        dataset = X if is_dataset(X) else self.get_dataset(X)
        iterator = self.get_iterator(dataset, training=training)
        for Xi, _ in iterator:
            yp = self.evaluation_step(Xi, training=training)
            if isinstance(yp, tuple):
                yield tuple(n.to(device) for n in yp)
            else:
                yield yp.to(device)