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,
      use_cache=None,
      output_attentions=None,
      output_hidden_states=None,
      return_dict=None,
  ):
      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
      )
      use_cache = (
          use_cache if use_cache is not None else self.config.use_cache
      )
      return_dict = (
          return_dict
          if return_dict is not None
          else self.config.use_return_dict
      )
  
      if input_ids is not None and inputs_embeds is not None:
          raise ValueError(
              "You cannot specify both input_ids and inputs_embeds at the same time"
          )
      elif input_ids is not None:
          input_shape = input_ids.size()
          input_ids = input_ids.view(-1, input_shape[-1])
          batch_size = input_ids.shape[0]
      elif inputs_embeds is not None:
          input_shape = inputs_embeds.size()[:-1]
          batch_size = inputs_embeds.shape[0]
      else:
          raise ValueError(
              "You have to specify either input_ids or inputs_embeds"
          )
  
      device = (
          input_ids.device
          if input_ids is not None
          else inputs_embeds.device
      )
  
      if token_type_ids is not None:
          token_type_ids = token_type_ids.view(-1, input_shape[-1])
      if position_ids is not None:
          position_ids = position_ids.view(-1, input_shape[-1])
  
      if past_key_values is None:
          past_length = 0
          past_key_values = tuple([None] * len(self.h))
      else:
          past_length = past_key_values[0][0].size(-2)
      if position_ids is None:
          position_ids = torch.arange(
              past_length,
              input_shape[-1] + past_length,
              dtype=torch.long,
              device=device,
          )
          position_ids = position_ids.unsqueeze(0).view(
              -1, input_shape[-1]
          )
  
      # GPT2Attention mask.
      if attention_mask is not None:
          if batch_size <= 0:
              raise ValueError("batch_size has to be defined and > 0")
          attention_mask = attention_mask.view(batch_size, -1)
          # We create a 3D attention mask from a 2D tensor mask.
          # Sizes are [batch_size, 1, 1, to_seq_length]
          # So we can broadcast to [batch_size, num_heads, from_seq_length, to_seq_length]
          # this attention mask is more simple than the triangular masking of causal attention
          # used in OpenAI GPT, we just need to prepare the broadcast dimension here.
          attention_mask = attention_mask[:, None, None, :]
  
          # Since attention_mask is 1.0 for positions we want to attend and 0.0 for
          # masked positions, this operation will create a tensor which is 0.0 for
          # positions we want to attend and -10000.0 for masked positions.
          # Since we are adding it to the raw scores before the softmax, this is
          # effectively the same as removing these entirely.
          attention_mask = attention_mask.to(
              dtype=self.dtype
          )  # fp16 compatibility
          attention_mask = (1.0 - attention_mask) * -10000.0
  
      # If a 2D ou 3D attention mask is provided for the cross-attention
      # we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
      if (
          self.config.add_cross_attention
          and encoder_hidden_states is not None
      ):
          (
              encoder_batch_size,
              encoder_sequence_length,
              _,
          ) = encoder_hidden_states.size()
          encoder_hidden_shape = (
              encoder_batch_size,
              encoder_sequence_length,
          )
          if encoder_attention_mask is None:
              encoder_attention_mask = torch.ones(
                  encoder_hidden_shape, device=device
              )
          encoder_attention_mask = self.invert_attention_mask(
              encoder_attention_mask
          )
      else:
          encoder_attention_mask = None
  
      # Prepare head mask if needed
      # 1.0 in head_mask indicate we keep the head
      # attention_probs has shape bsz x n_heads x N x N
      # head_mask has shape n_layer x batch x n_heads x N x N
      head_mask = self.get_head_mask(head_mask, self.config.n_layer)
  
      if inputs_embeds is None:
          inputs_embeds = self.wte(input_ids)
      position_embeds = self.wpe(position_ids)
      hidden_states = inputs_embeds + position_embeds
  
      if token_type_ids is not None:
          token_type_embeds = self.wte(token_type_ids)
          hidden_states = hidden_states + token_type_embeds
  
      hidden_states = self.drop(hidden_states)
  
      output_shape = input_shape + (hidden_states.size(-1),)
  
      presents = () if use_cache else None
      all_self_attentions = () if output_attentions else None
      all_cross_attentions = (
          ()
          if output_attentions and self.config.add_cross_attention
          else None
      )
      all_hidden_states = () if output_hidden_states else None
      for i, (block, layer_past) in enumerate(
          zip(self.h, past_key_values)
      ):
          # Model parallel
          if self.model_parallel:
              torch.cuda.set_device(hidden_states.device)
              # Ensure layer_past is on same device as hidden_states (might not be correct)
              if layer_past is not None:
                  layer_past = tuple(
                      past_state.to(hidden_states.device)
                      for past_state in layer_past
                  )
              # Ensure that attention_mask is always on the same device as hidden_states
              if attention_mask is not None:
                  attention_mask = attention_mask.to(
                      hidden_states.device
                  )
              if isinstance(head_mask, torch.Tensor):
                  head_mask = head_mask.to(hidden_states.device)
          if output_hidden_states:
              all_hidden_states = all_hidden_states + (hidden_states,)
  
          if self.gradient_checkpointing and self.training:
              if use_cache:
                  logger.warning(
                      "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
                  )
                  use_cache = False
  
              def create_custom_forward(module):
                  def custom_forward(*inputs):
                      # None for past_key_value
                      return module(
                          *inputs, use_cache, output_attentions
                      )
  
                  return custom_forward
  
              outputs = torch.utils.checkpoint.checkpoint(
                  create_custom_forward(block),
                  hidden_states,
                  None,
                  attention_mask,
                  head_mask[i],
                  encoder_hidden_states,
                  encoder_attention_mask,
              )
          else:
              outputs = block(
                  hidden_states,
                  layer_past=layer_past,
                  attention_mask=attention_mask,
                  head_mask=head_mask[i],
                  encoder_hidden_states=encoder_hidden_states,
                  encoder_attention_mask=encoder_attention_mask,
                  use_cache=use_cache,
                  output_attentions=output_attentions,
              )
  
          hidden_states = outputs[0]
          if use_cache is True:
              presents = presents + (outputs[1],)
  
          if output_attentions:
              all_self_attentions = all_self_attentions + (
                  outputs[2 if use_cache else 1],
              )
              if self.config.add_cross_attention:
                  all_cross_attentions = all_cross_attentions + (
                      outputs[3 if use_cache else 2],
                  )
  
          # Model Parallel: If it's the last layer for that device, put things on the next device
          if self.model_parallel:
              for k, v in self.device_map.items():
                  if (
                      i == v[-1]
                      and "cuda:" + str(k) != self.last_device
                  ):
                      hidden_states = hidden_states.to(
                          "cuda:" + str(k + 1)
                      )
  
      hidden_states = self.ln_f(hidden_states)
  
      hidden_states = hidden_states.view(*output_shape)
      # Add last hidden state
      if output_hidden_states:
          all_hidden_states = all_hidden_states + (hidden_states,)
  
      if not return_dict:
          return tuple(
              v
              for v in [
                  hidden_states,
                  presents,
                  all_hidden_states,
                  all_self_attentions,
                  all_cross_attentions,
              ]
              if v is not None
          )
  
      return BaseModelOutputWithPastAndCrossAttentions(
          last_hidden_state=hidden_states,
          past_key_values=presents,
          hidden_states=all_hidden_states,
          attentions=all_self_attentions,
          cross_attentions=all_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 GPT2Model 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, use_cache, output_attentions, output_hidden_states, return_dict) and returns: BaseModelOutputWithPastAndCrossAttentions, tuple, custom_forward, module