code: def forward(
      self,
      hidden_states: torch.Tensor,
      attention_mask: Optional[torch.FloatTensor] = None,
      head_mask: Optional[torch.FloatTensor] = None,
      past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
      output_attentions: Optional[bool] = True,
  ):
      residual = hidden_states
      if output_attentions:
          hidden_states, attention_opt_weights = self.self_attn(
              hidden_states=hidden_states,
              attention_mask=attention_mask,
              head_mask=head_mask,
              past_key_value=past_key_value,
              output_attentions=output_attentions,
          )
  
      else:
          hidden_states = self.self_attn(
              hidden_states=hidden_states,
              attention_mask=attention_mask,
              head_mask=head_mask,
              past_key_value=past_key_value,
              output_attentions=output_attentions,
          )
      hidden_states = residual + self.dropout1(hidden_states)
      hidden_states = self.norm1(hidden_states)
      residual = hidden_states
  
      hidden_states = self.linear1(hidden_states)
      hidden_states = self.activation(hidden_states)
      hidden_states = self.dropout(hidden_states)
      hidden_states = self.linear2(hidden_states)
      hidden_states = residual + self.dropout2(hidden_states)
      hidden_states = self.norm2(hidden_states)
  
      if output_attentions:
          return hidden_states, attention_opt_weights
      else:
          return hidden_states
  
imports: import math
  from typing import List, Optional, Tuple, Union
  import torch
  import torch.utils.checkpoint
  from torch import nn, tensor
  from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
  from ...activations import ACT2FN
  from ...modeling_outputs import BaseModelOutputWithPastAndCrossAttentions, BaseModelOutputWithPoolingAndCrossAttentions, MultipleChoiceModelOutput, QuestionAnsweringModelOutput, SequenceClassifierOutput, TokenClassifierOutput
  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_ernie_m import ErnieMConfig
label_desc: Use image node_id 2 for calling the ErnieMEncoderLayer obj's underlying member method code with example usage: obj.forward(hidden_states, attention_mask, head_mask, past_key_value, output_attentions) and returns: hidden_states,  attention_opt_weights, hidden_states