class_docstr: Predicts a single float per each temporal location
class_docstr_tok: ["Predicts","a","single","float","per","each","temporal","location"]
code: def output_types(self):
      return {
          "out": NeuralType(("B", "T"), EncodedRepresentation()),
      }
  
imports: import torch
  from nemo.collections.tts.modules.submodules import ConditionalInput, ConditionalLayerNorm
  from nemo.collections.tts.parts.utils.helpers import binarize_attention_parallel, regulate_len
  from nemo.core.classes import NeuralModule, adapter_mixins, typecheck
  from nemo.core.neural_types.elements import EncodedRepresentation, Index, LengthsType, LogprobsType, MelSpectrogramType, ProbsType, RegressionValuesType, TokenDurationType, TokenIndex, TokenLogDurationType
  from nemo.core.neural_types.neural_type import NeuralType
label_desc: Use image node_id 3 for calling the TemporalPredictor obj's underlying member method code with example usage: obj.output_types() and returns: dict