code: def __init__(
      self,
      config,
      asp_hidden_dim: int = 3000,  # prefix param, avoid confusion with PLM params
      asp_dropout_rate: float = 0.3,
      asp_init_std: float = 0.02,
      asp_feature_emb_size: int = 20,
      asp_linking_distance_num_buckets: int = 16,
      asp_activation: str = "relu",
      mention_start_id: int = 0,
      mention_end_id: int = 0,
  ):
      super().__init__(config)
      self.t5 = T5Model(config)
      self.dropout = nn.Dropout(p=asp_dropout_rate)
      self.init_std = asp_init_std
  
      self.feature_emb_size = asp_feature_emb_size
      self.linking_distance_num_buckets = (
          asp_linking_distance_num_buckets
      )
      # output_dim: {0, 1, 2}
      # corresponding to 3 actions: {mention_end, mention_start, copy}
  
      self.action_head = util.make_ffnn(
          self.config.d_model,
          hidden_size=[asp_hidden_dim],
          output_size=1,
          dropout=self.dropout,
          std=self.init_std,
          activation=asp_activation,
      )
      self.lr_scorer = util.make_ffnn(
          2 * self.config.d_model,
          hidden_size=[asp_hidden_dim],
          output_size=1,
          dropout=self.dropout,
          std=self.init_std,
          activation=asp_activation,
      )
      self.rr_scorer = util.make_ffnn(
          2 * self.config.d_model + self.feature_emb_size,
          hidden_size=[asp_hidden_dim],
          output_size=1,
          dropout=self.dropout,
          std=self.init_std,
          activation=asp_activation,
      )
  
      # feature embeddings
      self.emb_rr_distance = util.make_embedding(
          self.linking_distance_num_buckets,
          feature_emb_size=self.feature_emb_size,
          std=self.init_std,
      )
  
      self.mention_start_id = mention_start_id
      self.mention_end_id = mention_end_id
  
      self.post_init()
  
imports: import sys
  import os
  from typing import Optional, Tuple, Any, Dict, Iterable
  import logging
  import torch
  import torch.utils.checkpoint
  from torch import nn
  import torch.nn.init
  import torch.nn.functional
  from transformers.models.t5.modeling_t5 import T5PreTrainedModel, T5Model, T5_INPUTS_DOCSTRING, _CONFIG_FOR_DOC
  from transformers.file_utils import add_start_docstrings_to_model_forward, replace_return_docstrings
  from .modeling_outputs import MySeq2SeqLMOutput
  from util import logsumexp, dummy_padding, prepare_pair_embeddings, one_hot_ignore_negative, batched_masked_select
label_desc: Use image node_id 1 to create a new T5Coref object from inherited base classes: T5PreTrainedModel with example: obj = T5Coref(config, asp_hidden_dim, asp_dropout_rate, asp_init_std, asp_feature_emb_size, asp_linking_distance_num_buckets, asp_activation, mention_start_id, mention_end_id)