code: def input_types(self):
      return {
          "attn_logprob": NeuralType(
              ("B", "S", "T_spec", "T_text"), LogprobsType()
          ),
          "in_lens": NeuralType(tuple("B"), LengthsType()),
          "out_lens": NeuralType(tuple("B"), LengthsType()),
      }
  
file_path: NeMo/nemo/collections/tts/losses/aligner_loss.py
imports: import torch
  import torch.nn.functional
  from nemo.core.classes import Loss, typecheck
  from nemo.core.neural_types.elements import LengthsType, LogprobsType, LossType, ProbsType
  from nemo.core.neural_types.neural_type import NeuralType
label_desc: Use image node_id 2 for calling the ForwardSumLoss obj's underlying member method code with example usage: obj.input_types() and returns: dict