*Outputs* a tuple of (last_hidden_state, new_mems)
*Outputs* a tuple of (last_hidden_state, new_mems)
-`softmax_output`: output of the (adaptive) softmax:
-`softmax_output`: output of the (adaptive) softmax:
- if target is None: Negative log likelihood of shape [batch_size, sequence_length]
- if target is None: log probabilities of tokens, shape [batch_size, sequence_length, n_tokens]
- else: log probabilities of tokens, shape [batch_size, sequence_length, n_tokens]
- else: Negative log likelihood of target tokens with shape [batch_size, sequence_length]
-`new_mems`: list (num layers) of updated mem states at the entry of each layer each mem state is a torch.FloatTensor of size [self.config.mem_len, batch_size, self.config.d_model]. Note that the first two dimensions are transposed in `mems` with regards to `input_ids`.
-`new_mems`: list (num layers) of updated mem states at the entry of each layer each mem state is a torch.FloatTensor of size [self.config.mem_len, batch_size, self.config.d_model]. Note that the first two dimensions are transposed in `mems` with regards to `input_ids`.
