class_docstr: Pure Python implementation of TDT loss (https://arxiv.org/pdf/2304.06795.pdf)
class_docstr_tok: ["Pure","Python","implementation","of","TDT","loss","(","https",":","\/\/arxiv.org\/pdf\/2304.06795.pdf",")"]
docstr: Input types definitions for CTCLoss.
          
docstr_tok: ["Input","types","definitions","for","CTCLoss","."]
code: def input_types(self):
      """Input types definitions for CTCLoss."""
      return {
          "acts": NeuralType(("B", "T", "T", "D"), LogprobsType()),
          "labels": NeuralType(("B", "T"), LabelsType()),
          "act_lens": NeuralType(tuple("B"), LengthsType()),
          "label_lens": NeuralType(tuple("B"), LengthsType()),
      }
  
imports: from typing import List
  import torch
  from nemo.core.classes import Loss
  from nemo.core.neural_types import LabelsType, LengthsType, LogprobsType, LossType, NeuralType
label_desc: Use image node_id 1 for calling the TDTLossPytorch obj's underlying member method code with example usage: obj.input_types() and returns: dict