code: def forward(
      self,
      hidden_states: torch.Tensor,
      head_mask: Optional[torch.Tensor] = None,
      output_attentions: bool = False,
      output_hidden_states: bool = False,
      return_dict: bool = True,
  ) -> Union[tuple, BaseModelOutput]:
      all_hidden_states = () if output_hidden_states else None
      all_self_attentions = () if output_attentions else None
  
      for i, layer_module in enumerate(self.layer):
          if output_hidden_states:
              all_hidden_states = all_hidden_states + (hidden_states,)
  
          layer_head_mask = (
              head_mask[i] if head_mask is not None else None
          )
  
          if self.gradient_checkpointing and self.training:
              layer_outputs = self._gradient_checkpointing_func(
                  layer_module.__call__,
                  hidden_states,
                  layer_head_mask,
                  output_attentions,
              )
          else:
              layer_outputs = layer_module(
                  hidden_states, layer_head_mask, output_attentions
              )
  
          hidden_states = layer_outputs[0]
  
          if output_attentions:
              all_self_attentions = all_self_attentions + (
                  layer_outputs[1],
              )
  
      if output_hidden_states:
          all_hidden_states = all_hidden_states + (hidden_states,)
  
      if not return_dict:
          return tuple(
              v
              for v in [
                  hidden_states,
                  all_hidden_states,
                  all_self_attentions,
              ]
              if v is not None
          )
      return BaseModelOutput(
          last_hidden_state=hidden_states,
          hidden_states=all_hidden_states,
          attentions=all_self_attentions,
      )
  
imports: import math
  from typing import Dict, List, Optional, Set, 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 BaseModelOutput, BaseModelOutputWithPooling, SequenceClassifierOutput
  from ...modeling_utils import PreTrainedModel
  from ...pytorch_utils import find_pruneable_heads_and_indices, prune_linear_layer
  from ...utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging
  from .configuration_audio_spectrogram_transformer import ASTConfig
label_desc: Use image node_id 2 for calling the ASTEncoder obj's underlying member method code with example usage: obj.forward(hidden_states, head_mask, output_attentions, output_hidden_states, return_dict) and returns: BaseModelOutput, tuple