code: def forward(
      self,
      hidden_states: torch.Tensor,
      attention_mask: Optional[torch.FloatTensor] = None,
      head_mask: Optional[torch.FloatTensor] = None,
      encoder_hidden_states: Optional[torch.FloatTensor] = None,
      encoder_attention_mask: Optional[torch.FloatTensor] = None,
      past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
      output_attentions: Optional[bool] = False,
  ) -> Tuple[torch.Tensor]:
      self_outputs = self.self_attn(
          hidden_states,
          attention_mask,
          head_mask,
          encoder_hidden_states,
          encoder_attention_mask,
          past_key_value,
          output_attentions,
      )
      attention_output = self.out_proj(self_outputs[0])
      outputs = (attention_output,) + self_outputs[
          1:
      ]  # add attentions if we output them
      return outputs
  
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 3 for calling the ErnieMAttention obj's underlying member method code with example usage: obj.forward(hidden_states, attention_mask, head_mask, encoder_hidden_states, encoder_attention_mask, past_key_value, output_attentions) and returns: outputs