class_docstr: Module for classifying text used in original paper code.
      
class_docstr_tok: ["Module","for","classifying","text","used","in","original","paper","code","."]
docstr: Initialize a ClassifierModule.
  
  :param args: model structure parameters and hyperparameters
  :type args: ModelArguments
  :param word_vocab: a mapping of a set of words (keys) to
      indices (values)
  :type word_vocab: dict
docstr_tok: ["Initialize","a","ClassifierModule",".",":","param","args",":","model","structure","parameters","and","hyperparameters",":","type","args",":","ModelArguments",":","param","word_vocab",":","a","mapping","of","a","set","of","words","(","keys",")","to","indices","(","values",")",":","type","word_vocab",":","dict"]
code: def __init__(self, args, word_vocab):
      """Initialize a ClassifierModule.
  
      :param args: model structure parameters and hyperparameters
      :type args: ModelArguments
      :param word_vocab: a mapping of a set of words (keys) to
          indices (values)
      :type word_vocab: dict
      """
      super(ClassifierModule, self).__init__()
      self.args = args
      self.encoder = RnnModel(
          self.args.embedding_dim,
          self.args.hidden_dim,
          self.args.layer_num,
          self.args.dropout_rate,
      )
      self.predictor = nn.Linear(
          self.args.hidden_dim, self.args.num_labels
      )
  
      self.input_dim = args.embedding_dim
      self.embedding_path = args.embedding_path
      self.fine_tuning = args.fine_tuning
  
      self.init_embedding_layer(word_vocab)
      self.NEG_INF = -1.0e6
  
imports: import os
  import logging
  import numpy
  import torch
  import torch.nn
  from torch.autograd import Variable
  from tqdm import tqdm
  from interpret_text.experimental.common.utils_introspective_rationale import generate_data
label_desc: Use image node_id 1 to create a new ClassifierModule object from inherited base classes: nn with example: obj = ClassifierModule(args, word_vocab)