class_docstr: Predicts a single float per each temporal location
class_docstr_tok: ["Predicts","a","single","float","per","each","temporal","location"]
code: def __init__(
      self,
      input_size,
      filter_size,
      kernel_size,
      dropout,
      n_layers=2,
      condition_types=[],
  ):
      super(TemporalPredictor, self).__init__()
      self.cond_input = ConditionalInput(
          input_size, input_size, condition_types
      )
      self.layers = torch.nn.ModuleList()
      for i in range(n_layers):
          self.layers.append(
              ConvReLUNorm(
                  input_size if i == 0 else filter_size,
                  filter_size,
                  kernel_size=kernel_size,
                  dropout=dropout,
                  condition_dim=input_size,
                  condition_types=condition_types,
              )
          )
      self.fc = torch.nn.Linear(filter_size, 1, bias=True)
  
      # Use for adapter input dimension
      self.filter_size = filter_size
  
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 1 to create a new TemporalPredictor object from inherited base classes: NeuralModule with example: obj = TemporalPredictor(input_size, filter_size, kernel_size, dropout, n_layers, condition_types)