class_docstr: Args:
      hidden_size: the hidden size of the mlp head on the top of the encoder
      num_layers: number of the linear layers of the mlp head on the top of the encoder
      activation: type of activations between layers of the mlp head
      dropout: the dropout used for the mlp head
      use_transformer_init: initializes the weights with the same approach used in Transformer
      idx_conditioned_on: index of the token to use as the sequence representation for the classification task,
          default is the first token
class_docstr_tok: ["Args",":","hidden_size",":","the","hidden","size","of","the","mlp","head","on","the","top","of","the","encoder","num_layers",":","number","of","the","linear","layers","of","the","mlp","head","on","the","top","of","the","encoder","activation",":","type","of","activations","between","layers","of","the","mlp","head","dropout",":","the","dropout","used","for","the","mlp","head","use_transformer_init",":","initializes","the","weights","with","the","same","approach","used","in","Transformer","idx_conditioned_on",":","index","of","the","token","to","use","as","the","sequence","representation","for","the","classification","task",",","default","is","the","first","token"]
docstr: Initializes the SequenceRegression module. 
docstr_tok: ["Initializes","the","SequenceRegression","module","."]
code: def __init__(
      self,
      hidden_size: int,
      num_layers: int = 2,
      activation: str = "relu",
      dropout: float = 0.0,
      use_transformer_init: bool = True,
      idx_conditioned_on: int = 0,
  ):
      """Initializes the SequenceRegression module."""
      super().__init__(hidden_size=hidden_size, dropout=dropout)
      self._idx_conditioned_on = idx_conditioned_on
      self.mlp = MultiLayerPerceptron(
          hidden_size,
          num_classes=1,
          num_layers=num_layers,
          activation=activation,
          log_softmax=False,
      )
      self.post_init(use_transformer_init=use_transformer_init)
  
file_path: NeMo/nemo/collections/nlp/modules/common/sequence_regression.py
imports: from typing import Dict, Optional
  from torch import Tensor
  from nemo.collections.common.parts import MultiLayerPerceptron
  from nemo.collections.nlp.modules.common.classifier import Classifier
  from nemo.core.classes import typecheck
  from nemo.core.neural_types import NeuralType, RegressionValuesType
label_desc: Use image node_id 2 to create a new SequenceRegression object from inherited base classes: Classifier with example: obj = SequenceRegression(hidden_size, num_layers, activation, dropout, use_transformer_init, idx_conditioned_on)