code: def __init__(self, config):
      super(ErnieMForInformationExtraction, self).__init__(config)
      self.ernie_m = ErnieMModel(config)
      self.linear_start = nn.Linear(config.hidden_size, 1)
      self.linear_end = nn.Linear(config.hidden_size, 1)
      self.sigmoid = nn.Sigmoid()
      self.post_init()
  
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 ErnieMForInformationExtraction object from inherited base classes: ErnieMPreTrainedModel with example: obj = ErnieMForInformationExtraction(config)