code: def __init__(self, config, position_embedding_type=None):
      super().__init__()
      self.self_attn = ErnieMSelfAttention(
          config, position_embedding_type=position_embedding_type
      )
      self.out_proj = nn.Linear(config.hidden_size, config.hidden_size)
      self.pruned_heads = set()
  
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 1 to create a new ErnieMAttention object from inherited base classes: nn with example: obj = ErnieMAttention(config, position_embedding_type)