docstr: labels (`torch.LongTensor` of shape `(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","(","`","torch.LongTensor","`","of","shape","`","(","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: Optional[torch.LongTensor] = None,
      past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None,
      attention_mask: Optional[torch.FloatTensor] = None,
      token_type_ids: Optional[torch.LongTensor] = None,
      position_ids: Optional[torch.LongTensor] = None,
      head_mask: Optional[torch.FloatTensor] = None,
      inputs_embeds: Optional[torch.FloatTensor] = None,
      labels: Optional[torch.LongTensor] = None,
      use_cache: Optional[bool] = None,
      output_attentions: Optional[bool] = None,
      output_hidden_states: Optional[bool] = None,
      return_dict: Optional[bool] = None,
  ) -> Union[Tuple, CausalLMOutputWithPast]:
      r"""
      labels (`torch.LongTensor` of shape `(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,
          use_cache=use_cache,
          output_attentions=output_attentions,
          output_hidden_states=output_hidden_states,
          return_dict=return_dict,
      )
      hidden_states = transformer_outputs[0]
  
      # make sure sampling in fp16 works correctly and
      # compute loss in fp32 to match with mesh-tf version
      # https://github.com/EleutherAI/gpt-neo/blob/89ce74164da2fb16179106f54e2269b5da8db333/models/gpt2/gpt2.py#L179
      lm_logits = self.lm_head(hidden_states).to(torch.float32)
  
      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),
          )
  
          loss = loss.to(hidden_states.dtype)
  
      if not return_dict:
          output = (lm_logits,) + transformer_outputs[1:]
          return ((loss,) + output) if loss is not None else output
  
      return CausalLMOutputWithPast(
          loss=loss,
          logits=lm_logits,
          past_key_values=transformer_outputs.past_key_values,
          hidden_states=transformer_outputs.hidden_states,
          attentions=transformer_outputs.attentions,
      )
  
imports: from typing import Optional, Tuple, Union
  import torch
  import torch.utils.checkpoint
  from torch import nn
  from torch.nn import CrossEntropyLoss
  from ...activations import ACT2FN
  from ...modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast
  from ...modeling_utils import PreTrainedModel
  from ...utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging
  from .configuration_codegen import CodeGenConfig
label_desc: Use image node_id 5 for calling the CodeGenForCausalLM 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, labels, use_cache, output_attentions, output_hidden_states, return_dict) and returns: CausalLMOutputWithPast, unknown