class_docstr: Predicts a single float per each temporal location
class_docstr_tok: ["Predicts","a","single","float","per","each","temporal","location"]
code: def forward(self, enc, enc_mask, conditioning=None):
      enc = self.cond_input(enc, conditioning)
      out = enc * enc_mask
      out = out.transpose(1, 2)
  
      for layer in self.layers:
          out = layer(out, conditioning=conditioning)
  
      out = out.transpose(1, 2)
      out = self.fc(out) * enc_mask
      return out.squeeze(-1)
  
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 4 for calling the TemporalPredictor obj's underlying member method code with example usage: obj.forward(enc, enc_mask, conditioning) and returns: out