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

* numpy arrays
            * torch tensors
            * pandas DataFrame or Series
            * 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.

        training : bool (default=False)
          Whether train mode should be used or not.

        """
        y_true = to_tensor(y_true, device=self.device)
        return self.criterion_(y_pred, y_true)

def infer(self, x, **fit_params):
        """Perform a single inference step on a batch of data.

        Parameters
        ----------
        x : input data
          A batch of the input data.

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

        """
        x = to_tensor(x, device=self.device)
        if isinstance(x, dict):
            x_dict = self._merge_x_and_fit_params(x, fit_params)
            return self.module_(**x_dict)
        return self.module_(x, **fit_params)

* 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.

        training : bool (default=False)
          Whether train mode should be used or not.

        """
        y_true = to_tensor(y_true, device=self.device)
        return self.criterion_(y_pred, y_true)