How to use the skorch.dataset.uses_placeholder_y function in skorch

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

        """
        self.check_data(X, y)
        epochs = epochs if epochs is not None else self.max_epochs

        dataset_train, dataset_valid = self.get_split_datasets(
            X, y, **fit_params)
        on_epoch_kwargs = {
            'dataset_train': dataset_train,
            'dataset_valid': dataset_valid,
        }

        y_train_is_ph = uses_placeholder_y(dataset_train)
        y_valid_is_ph = uses_placeholder_y(dataset_valid)

        for _ in range(epochs):
            self.notify('on_epoch_begin', **on_epoch_kwargs)

            for Xi, yi in self.get_iterator(dataset_train, training=True):
                yi_res = yi if not y_train_is_ph else None
                self.notify('on_batch_begin', X=Xi, y=yi_res, training=True)
                step = self.train_step(Xi, yi, **fit_params)
                self.history.record_batch('train_loss', step['loss'].item())
                self.history.record_batch('train_batch_size', get_len(Xi))
                self.notify('on_batch_end', X=Xi, y=yi_res, training=True, **step)

            if dataset_valid is None:
                self.notify('on_epoch_end', **on_epoch_kwargs)
                continue

Additional parameters passed to the ``forward`` method of
          the module and to the ``self.train_split`` call.

        """
        self.check_data(X, y)
        epochs = epochs if epochs is not None else self.max_epochs

        dataset_train, dataset_valid = self.get_split_datasets(
            X, y, **fit_params)
        on_epoch_kwargs = {
            'dataset_train': dataset_train,
            'dataset_valid': dataset_valid,
        }

        y_train_is_ph = uses_placeholder_y(dataset_train)
        y_valid_is_ph = uses_placeholder_y(dataset_valid)

        for _ in range(epochs):
            self.notify('on_epoch_begin', **on_epoch_kwargs)

            train_batch_count = 0
            for data in self.get_iterator(dataset_train, training=True):
                Xi, yi = unpack_data(data)
                yi_res = yi if not y_train_is_ph else None
                self.notify('on_batch_begin', X=Xi, y=yi_res, training=True)
                step = self.train_step(Xi, yi, **fit_params)
                train_batch_count += 1
                self.history.record_batch('train_loss', step['loss'].item())
                self.history.record_batch('train_batch_size', get_len(Xi))
                self.notify('on_batch_end', X=Xi, y=yi_res, training=True, **step)
            self.history.record("train_batch_count", train_batch_count)

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

        """
        self.check_data(X, y)
        epochs = epochs if epochs is not None else self.max_epochs

        dataset_train, dataset_valid = self.get_split_datasets(
            X, y, **fit_params)
        on_epoch_kwargs = {
            'dataset_train': dataset_train,
            'dataset_valid': dataset_valid,
        }

        y_train_is_ph = uses_placeholder_y(dataset_train)
        y_valid_is_ph = uses_placeholder_y(dataset_valid)

        for _ in range(epochs):
            self.notify('on_epoch_begin', **on_epoch_kwargs)

            train_batch_count = 0
            for data in self.get_iterator(dataset_train, training=True):
                Xi, yi = unpack_data(data)
                yi_res = yi if not y_train_is_ph else None
                self.notify('on_batch_begin', X=Xi, y=yi_res, training=True)
                step = self.train_step(Xi, yi, **fit_params)
                train_batch_count += 1
                self.history.record_batch('train_loss', step['loss'].item())
                self.history.record_batch('train_batch_size', get_len(Xi))
                self.notify('on_batch_end', X=Xi, y=yi_res, training=True, **step)
            self.history.record("train_batch_count", train_batch_count)

Additional parameters passed to the ``forward`` method of
          the module and to the ``self.train_split`` call.

        """
        self.check_data(X, y)
        epochs = epochs if epochs is not None else self.max_epochs

        dataset_train, dataset_valid = self.get_split_datasets(
            X, y, **fit_params)
        on_epoch_kwargs = {
            'dataset_train': dataset_train,
            'dataset_valid': dataset_valid,
        }

        y_train_is_ph = uses_placeholder_y(dataset_train)
        y_valid_is_ph = uses_placeholder_y(dataset_valid)

        for _ in range(epochs):
            self.notify('on_epoch_begin', **on_epoch_kwargs)

            for Xi, yi in self.get_iterator(dataset_train, training=True):
                yi_res = yi if not y_train_is_ph else None
                self.notify('on_batch_begin', X=Xi, y=yi_res, training=True)
                step = self.train_step(Xi, yi, **fit_params)
                self.history.record_batch('train_loss', step['loss'].item())
                self.history.record_batch('train_batch_size', get_len(Xi))
                self.notify('on_batch_end', X=Xi, y=yi_res, training=True, **step)

            if dataset_valid is None:
                self.notify('on_epoch_end', **on_epoch_kwargs)
                continue