docstr: Args:
      input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
          Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you
          provide it.
  
          Indices can be obtained using [`OPTTokenizer`]. See [`PreTrainedTokenizer.encode`] and
          [`PreTrainedTokenizer.__call__`] for details.
  
          [What are input IDs?](../glossary#input-ids)
      attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
          Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
  
          - 1 for tokens that are **not masked**,
          - 0 for tokens that are **masked**.
  
          [What are attention masks?](../glossary#attention-mask)
      head_mask (`torch.Tensor` of shape `(num_hidden_layers, num_attention_heads)`, *optional*):
          Mask to nullify selected heads of the attention modules. Mask values selected in `[0, 1]`:
  
          - 1 indicates the head is **not masked**,
          - 0 indicates the head is **masked**.
  
      past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
          Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of
          shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of
          shape `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`. The two additional
          tensors are only required when the model is used as a decoder in a Sequence to Sequence model.
  
          Contains pre-computed hidden-states (key and values in the self-attention blocks and in the
          cross-attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.
  
          If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those
          that don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of
          all `decoder_input_ids` of shape `(batch_size, sequence_length)`.
      inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
          Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation.
          This is useful if you want more control over how to convert `input_ids` indices into associated vectors
          than the model's internal embedding lookup matrix.
      labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
          Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
          config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
          (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
      use_cache (`bool`, *optional*):
          If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
          (see `past_key_values`).
      output_attentions (`bool`, *optional*):
          Whether or not to return the attentions tensors of all attention layers. See `attentions` under
          returned tensors for more detail.
      output_hidden_states (`bool`, *optional*):
          Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
          for more detail.
      return_dict (`bool`, *optional*):
          Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
  
  Returns:
  
  Example:
  
  ```python
  >>> from transformers import GPT2Tokenizer, OPTForCausalLM
  
  >>> model = OPTForCausalLM.from_pretrained("facebook/opt-350m")
  >>> tokenizer = GPT2Tokenizer.from_pretrained("facebook/opt-350m")
  
  >>> prompt = "Hey, are you consciours? Can you talk to me?"
  >>> inputs = tokenizer(prompt, return_tensors="pt")
  
  >>> # Generate
  >>> generate_ids = model.generate(inputs.input_ids, max_length=30)
  >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
  "Hey, are you consciours? Can you talk to me?\nI'm not consciours, but I can talk to you."
  ```
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code: def forward(
      self,
      input_ids: torch.LongTensor = None,
      attention_mask: Optional[torch.Tensor] = None,
      head_mask: Optional[torch.Tensor] = None,
      past_key_values: Optional[List[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"""
      Args:
          input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
              Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you
              provide it.
  
              Indices can be obtained using [`OPTTokenizer`]. See [`PreTrainedTokenizer.encode`] and
              [`PreTrainedTokenizer.__call__`] for details.
  
              [What are input IDs?](../glossary#input-ids)
          attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
              Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
  
              - 1 for tokens that are **not masked**,
              - 0 for tokens that are **masked**.
  
              [What are attention masks?](../glossary#attention-mask)
          head_mask (`torch.Tensor` of shape `(num_hidden_layers, num_attention_heads)`, *optional*):
              Mask to nullify selected heads of the attention modules. Mask values selected in `[0, 1]`:
  
              - 1 indicates the head is **not masked**,
              - 0 indicates the head is **masked**.
  
          past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
              Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of
              shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of
              shape `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`. The two additional
              tensors are only required when the model is used as a decoder in a Sequence to Sequence model.
  
              Contains pre-computed hidden-states (key and values in the self-attention blocks and in the
              cross-attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.
  
              If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those
              that don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of
              all `decoder_input_ids` of shape `(batch_size, sequence_length)`.
          inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
              Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation.
              This is useful if you want more control over how to convert `input_ids` indices into associated vectors
              than the model's internal embedding lookup matrix.
          labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
              Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
              config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
              (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
          use_cache (`bool`, *optional*):
              If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
              (see `past_key_values`).
          output_attentions (`bool`, *optional*):
              Whether or not to return the attentions tensors of all attention layers. See `attentions` under
              returned tensors for more detail.
          output_hidden_states (`bool`, *optional*):
              Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
              for more detail.
          return_dict (`bool`, *optional*):
              Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
  
      Returns:
  
      Example:
  
      ```python
      >>> from transformers import GPT2Tokenizer, OPTForCausalLM
  
      >>> model = OPTForCausalLM.from_pretrained("facebook/opt-350m")
      >>> tokenizer = GPT2Tokenizer.from_pretrained("facebook/opt-350m")
  
      >>> prompt = "Hey, are you consciours? Can you talk to me?"
      >>> inputs = tokenizer(prompt, return_tensors="pt")
  
      >>> # Generate
      >>> generate_ids = model.generate(inputs.input_ids, max_length=30)
      >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
      "Hey, are you consciours? Can you talk to me?\nI'm not consciours, but I can talk to you."
      ```"""
  
      output_attentions = (
          output_attentions
          if output_attentions is not None
          else self.config.output_attentions
      )
      output_hidden_states = (
          output_hidden_states
          if output_hidden_states is not None
          else self.config.output_hidden_states
      )
      return_dict = (
          return_dict
          if return_dict is not None
          else self.config.use_return_dict
      )
  
      # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
      outputs = self.model.decoder(
          input_ids=input_ids,
          attention_mask=attention_mask,
          head_mask=head_mask,
          past_key_values=past_key_values,
          inputs_embeds=inputs_embeds,
          use_cache=use_cache,
          output_attentions=output_attentions,
          output_hidden_states=output_hidden_states,
          return_dict=return_dict,
      )
  
      logits = self.lm_head(outputs[0]).contiguous()
  
      loss = None
      if labels is not None:
          # Shift so that tokens < n predict n
          shift_logits = logits[..., :-1, :].contiguous()
          shift_labels = labels[..., 1:].contiguous()
          # Flatten the tokens
          loss_fct = CrossEntropyLoss()
          loss = loss_fct(
              shift_logits.view(-1, self.config.vocab_size),
              shift_labels.view(-1),
          )
  
      if not return_dict:
          output = (logits,) + outputs[1:]
          return (loss,) + output if loss is not None else output
  
      return CausalLMOutputWithPast(
          loss=loss,
          logits=logits,
          past_key_values=outputs.past_key_values,
          hidden_states=outputs.hidden_states,
          attentions=outputs.attentions,
      )
  
imports: import random
  from typing import List, Optional, Tuple, Union
  import torch
  import torch.utils.checkpoint
  from torch import nn
  from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
  from ...activations import ACT2FN
  from ...modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast, QuestionAnsweringModelOutput, SequenceClassifierOutputWithPast
  from ...modeling_utils import PreTrainedModel
  from ...utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings
  from .configuration_opt import OPTConfig
label_desc: Use image node_id 8 for calling the OPTForCausalLM obj's underlying member method code with example usage: obj.forward(input_ids, attention_mask, head_mask, past_key_values, inputs_embeds, labels, use_cache, output_attentions, output_hidden_states, return_dict) and returns: CausalLMOutputWithPast, unknown