code: def forward(
      self,
      input_ids: Optional[torch.LongTensor] = None,
      attention_mask: Optional[torch.FloatTensor] = None,
      head_mask: Optional[torch.FloatTensor] = None,
      inputs_embeds: Optional[torch.FloatTensor] = None,
      past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None,
      use_cache: Optional[bool] = None,
      output_attentions: Optional[bool] = None,
      output_hidden_states: Optional[bool] = None,
      return_dict: Optional[bool] = None,
  ) -> Union[Tuple, BaseModelOutputWithPastAndCrossAttentions]:
      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
      )
  
      # retrieve input_ids and inputs_embeds
      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 = input_ids
          input_shape = input.size()
      elif inputs_embeds is not None:
          input_shape = inputs_embeds.size()[:-1]
          input = inputs_embeds[:, :, -1]
      else:
          raise ValueError(
              "You have to specify either input_ids or inputs_embeds"
          )
  
      # past_key_values_length
      past_key_values_length = (
          past_key_values[0][0].shape[2]
          if past_key_values is not None
          else 0
      )
  
      if inputs_embeds is None:
          inputs_embeds = self.embed_tokens(input) * self.embed_scale
  
      if attention_mask is None:
          attention_mask = torch.ones(
              (
                  inputs_embeds.shape[0],
                  inputs_embeds.shape[1] + past_key_values_length,
              ),
              dtype=torch.bool,
              device=inputs_embeds.device,
          )
      elif (
          attention_mask.shape[1]
          != past_key_values_length + input_shape[1]
      ):
          raise ValueError(
              f"The provided attention mask has length {attention_mask.shape[1]}, but its length should be "
              f"{past_key_values_length + input_shape[1]} (sum of the lengths of current and past inputs)"
          )
  
      # embed positions
      positions = self.embed_positions(
          attention_mask, past_key_values_length
      )
  
      attention_mask = _prepare_4d_causal_attention_mask(
          attention_mask,
          input_shape,
          inputs_embeds,
          past_key_values_length,
      )
  
      hidden_states = inputs_embeds + positions
  
      hidden_states = nn.functional.dropout(
          hidden_states, p=self.dropout, training=self.training
      )
  
      if self.gradient_checkpointing and self.training:
          if use_cache:
              logger.warning_once(
                  "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
              )
              use_cache = False
  
      all_hidden_states = () if output_hidden_states else None
      all_self_attns = () if output_attentions else None
      all_cross_attentions = None
      next_decoder_cache = () if use_cache else None
  
      for idx, decoder_layer in enumerate(self.layers):
          # add LayerDrop (see https://arxiv.org/abs/1909.11556 for description)
          if output_hidden_states:
              all_hidden_states += (hidden_states,)
          if self.training:
              dropout_probability = torch.rand([])
              if dropout_probability < self.layerdrop:
                  continue
  
          past_key_value = (
              past_key_values[idx]
              if past_key_values is not None
              else None
          )
  
          if self.gradient_checkpointing and self.training:
              layer_outputs = self._gradient_checkpointing_func(
                  decoder_layer.__call__,
                  hidden_states,
                  attention_mask,
                  head_mask[idx] if head_mask is not None else None,
                  None,
                  output_attentions,
                  use_cache,
              )
          else:
              layer_outputs = decoder_layer(
                  hidden_states,
                  attention_mask=attention_mask,
                  layer_head_mask=(
                      head_mask[idx] if head_mask is not None else None
                  ),
                  past_key_value=past_key_value,
                  output_attentions=output_attentions,
                  use_cache=use_cache,
              )
  
          hidden_states = layer_outputs[0]
  
          if use_cache:
              next_decoder_cache += (
                  layer_outputs[2 if output_attentions else 1],
              )
  
          if output_attentions:
              all_self_attns += (layer_outputs[1],)
  
      # add hidden states from the last decoder layer
      if output_hidden_states:
          all_hidden_states += (hidden_states,)
  
      hidden_states = self.layer_norm(hidden_states)
  
      next_cache = next_decoder_cache if use_cache else None
  
      if not return_dict:
          return tuple(
              v
              for v in [
                  hidden_states,
                  next_cache,
                  all_hidden_states,
                  all_self_attns,
                  all_cross_attentions,
              ]
              if v is not None
          )
      return BaseModelOutputWithPastAndCrossAttentions(
          last_hidden_state=hidden_states,
          past_key_values=next_cache,
          hidden_states=all_hidden_states,
          attentions=all_self_attns,
          cross_attentions=all_cross_attentions,
      )
  
imports: import math
  from typing import 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_attn_mask_utils import _prepare_4d_causal_attention_mask
  from ...modeling_outputs import BaseModelOutputWithPastAndCrossAttentions, CausalLMOutputWithCrossAttentions, SequenceClassifierOutputWithPast, TokenClassifierOutput
  from ...modeling_utils import PreTrainedModel
  from ...utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging
  from .configuration_biogpt import BioGptConfig
label_desc: Use image node_id 4 for calling the BioGptModel obj's underlying member method code with example usage: obj.forward(input_ids, attention_mask, head_mask, inputs_embeds, past_key_values, use_cache, output_attentions, output_hidden_states, return_dict) and returns: BaseModelOutputWithPastAndCrossAttentions, tuple