class_docstr: Calculate negative log likelihodd for sequence input, also applies label smoothing (if set).
class_docstr_tok: ["Calculate","negative","log","likelihodd","for","sequence","input",",","also","applies","label","smoothing","(","if","set",")","."]
docstr: Returns definitions of module input ports.
          
docstr_tok: ["Returns","definitions","of","module","input","ports","."]
code: def input_types(self):
      """Returns definitions of module input ports."""
      return {
          "log_probs": NeuralType(("B", "T", "D"), LogprobsType()),
          "labels": NeuralType(("B", "T"), LabelsType()),
          "output_mask": NeuralType(
              ("B", "T"), MaskType(), optional=True
          ),
          "lengths": NeuralType(("B"), LabelsType(), optional=True),
      }
  
file_path: NeMo/nemo/collections/common/losses/smoothed_cross_entropy.py
imports: from typing import Optional
  import torch
  from nemo.core.classes import Exportable, Loss, NeuralModule, typecheck
  from nemo.core.neural_types import LabelsType, LogprobsType, LossType, MaskType, NeuralType
label_desc: Use image node_id 1 for calling the SmoothedNLLLoss obj's underlying member method code with example usage: obj.input_types() and returns: dict