code: def __init__(
      self,
      config,
      num_ner_labels,
      head_hidden_dim=150,
      width_embedding_dim=150,
      max_span_length=8,
  ):
      super().__init__(config)
  
      self.albert = AlbertModel(config)
      self.hidden_dropout = nn.Dropout(config.hidden_dropout_prob)
      self.width_embedding = nn.Embedding(
          max_span_length + 1, width_embedding_dim
      )
  
      self.ner_classifier = nn.Sequential(
          FeedForward(
              input_dim=config.hidden_size * 2 + width_embedding_dim,
              num_layers=2,
              hidden_dims=head_hidden_dim,
              activations=F.relu,
              dropout=0.2,
          ),
          nn.Linear(head_hidden_dim, num_ner_labels),
      )
  
      self.init_weights()
  
imports: import os
  import sys
  import torch
  from torch import nn
  import torch.nn.functional
  from torch.nn import CrossEntropyLoss
  from allennlp.nn.util import batched_index_select
  from allennlp.nn import util
  from allennlp.modules import FeedForward
  import numpy
  from transformers import BertTokenizer, BertPreTrainedModel, BertModel
  from transformers import AlbertTokenizer, AlbertPreTrainedModel, AlbertModel
  import os
  import json
  import logging
label_desc: Use image node_id 1 to create a new AlbertForEntity object from inherited base classes: AlbertPreTrainedModel with example: obj = AlbertForEntity(config, num_ner_labels, head_hidden_dim, width_embedding_dim, max_span_length)