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"]
code: def output_types(self) -> Optional[Dict[str, NeuralType]]:
      return {"preds": NeuralType(tuple("B"), RegressionValuesType())}
  
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 1 for calling the SequenceRegression obj's underlying member method code with example usage: obj.output_types() and returns: dict