class_docstr: A baseclass for modules to perform various classification tasks.
class_docstr_tok: ["A","baseclass","for","modules","to","perform","various","classification","tasks","."]
docstr: Initializes the Classifier base module.
  Args:
      hidden_size: the size of the hidden dimension
      dropout: dropout to apply to the input hidden states
docstr_tok: ["Initializes","the","Classifier","base","module",".","Args",":","hidden_size",":","the","size","of","the","hidden","dimension","dropout",":","dropout","to","apply","to","the","input","hidden","states"]
code: def __init__(
      self,
      hidden_size: int,
      dropout: float = 0.0,
  ) -> None:
      """
      Initializes the Classifier base module.
      Args:
          hidden_size: the size of the hidden dimension
          dropout: dropout to apply to the input hidden states
      """
      super().__init__()
      self._hidden_size = hidden_size
      self.dropout = nn.Dropout(dropout)
  
file_path: NeMo/nemo/collections/nlp/modules/common/classifier.py
imports: from typing import Dict, Optional
  import torch
  from torch import nn
  from nemo.collections.common.parts import transformer_weights_init
  from nemo.core.classes import Exportable, NeuralModule
  from nemo.core.neural_types import ChannelType, NeuralType
label_desc: Use image node_id 2 to create a new Classifier object from inherited base classes: NeuralModule, Exportable with example: obj = Classifier(hidden_size, dropout)