docstr: labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
      Labels for language modeling. Note that the labels **are shifted** inside the model, i.e. you can set
      ``labels = input_ids`` Indices are selected in ``[-100, 0, ..., config.vocab_size]`` All labels set to
      ``-100`` are ignored (masked), the loss is only computed for labels in ``[0, ..., config.vocab_size]``
docstr_tok: ["labels","(",":","obj",":","`","torch.LongTensor","`","of","shape",":","obj",":","`","(","batch_size",",","sequence_length",")","`",",","`","optional","`",")",":","Labels","for","language","modeling",".","Note","that","the","labels","*","*","are","shifted","*","*","inside","the","model",",","i.e",".","you","can","set","``","labels","=","input_ids","``","Indices","are","selected","in","``","[","-100",",","0",",","...",",","config.vocab_size","]","``","All","labels","set","to","``","-100","``","are","ignored","(","masked",")",",","the","loss","is","only","computed","for","labels","in","``","[","0",",","...",",","config.vocab_size","]","``"]
code: def forward(
      self,
      input_ids=None,
      past_key_values=None,
      attention_mask=None,
      token_type_ids=None,
      position_ids=None,
      head_mask=None,
      inputs_embeds=None,
      encoder_hidden_states=None,
      encoder_attention_mask=None,
      labels=None,
      use_cache=None,
      output_attentions=None,
      output_hidden_states=None,
      return_dict=None,
  ):
      r"""
      labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
          Labels for language modeling. Note that the labels **are shifted** inside the model, i.e. you can set
          ``labels = input_ids`` Indices are selected in ``[-100, 0, ..., config.vocab_size]`` All labels set to
          ``-100`` are ignored (masked), the loss is only computed for labels in ``[0, ..., config.vocab_size]``
      """
      return_dict = (
          return_dict
          if return_dict is not None
          else self.config.use_return_dict
      )
  
      transformer_outputs = self.transformer(
          input_ids,
          past_key_values=past_key_values,
          attention_mask=attention_mask,
          token_type_ids=token_type_ids,
          position_ids=position_ids,
          head_mask=head_mask,
          inputs_embeds=inputs_embeds,
          encoder_hidden_states=encoder_hidden_states,
          encoder_attention_mask=encoder_attention_mask,
          use_cache=use_cache,
          output_attentions=output_attentions,
          output_hidden_states=output_hidden_states,
          return_dict=return_dict,
      )
      hidden_states = transformer_outputs[0]
  
      # Set device for model parallelism
      if self.model_parallel:
          torch.cuda.set_device(self.transformer.first_device)
          hidden_states = hidden_states.to(self.lm_head.weight.device)
  
      lm_logits = self.lm_head(hidden_states)
  
      loss = None
      if labels is not None:
          # Shift so that tokens < n predict n
          shift_logits = lm_logits[..., :-1, :].contiguous()
          shift_labels = labels[..., 1:].contiguous()
          # Flatten the tokens
          loss_fct = CrossEntropyLoss()
          loss = loss_fct(
              shift_logits.view(-1, shift_logits.size(-1)),
              shift_labels.view(-1),
          )
  
      if not return_dict:
          output = (lm_logits,) + transformer_outputs[1:]
          return ((loss,) + output) if loss is not None else output
  
      return CausalLMOutputWithCrossAttentions(
          loss=loss,
          logits=lm_logits,
          past_key_values=transformer_outputs.past_key_values,
          hidden_states=transformer_outputs.hidden_states,
          attentions=transformer_outputs.attentions,
          cross_attentions=transformer_outputs.cross_attentions,
      )
  
imports: from dataclasses import dataclass
  from typing import Optional, Tuple
  from .configuration_gpt2 import GPT2Config
  from fastNLP.transformers.torch.activations import ACT2FN
  from fastNLP.transformers.torch.file_utils import ModelOutput, add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, replace_return_docstrings
  from fastNLP.transformers.torch.modeling_outputs import BaseModelOutputWithPastAndCrossAttentions, CausalLMOutputWithCrossAttentions, SequenceClassifierOutputWithPast, TokenClassifierOutput
  from fastNLP.transformers.torch.modeling_utils import Conv1D, PreTrainedModel, SequenceSummary, find_pruneable_heads_and_indices, prune_conv1d_layer
  from fastNLP.transformers.torch.utils.model_parallel_utils import assert_device_map, get_device_map
  from fastNLP.envs.imports import _NEED_IMPORT_TORCH
  from fastNLP.core.log import logger
label_desc: Use image node_id 7 for calling the GPT2LMHeadModel obj's underlying member method code with example usage: obj.forward(input_ids, past_key_values, attention_mask, token_type_ids, position_ids, head_mask, inputs_embeds, encoder_hidden_states, encoder_attention_mask, labels, use_cache, output_attentions, output_hidden_states, return_dict) and returns: CausalLMOutputWithCrossAttentions, unknown