code: def __init__(
      self,
      in_channels,
      out_channels,
      kernel_size=1,
      dropout=0.0,
      condition_dim=384,
      condition_types=[],
  ):
      super(ConvReLUNorm, self).__init__()
      self.conv = torch.nn.Conv1d(
          in_channels,
          out_channels,
          kernel_size=kernel_size,
          padding=(kernel_size // 2),
      )
      self.norm = ConditionalLayerNorm(
          out_channels,
          condition_dim=condition_dim,
          condition_types=condition_types,
      )
      self.dropout = torch.nn.Dropout(dropout)
  
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 ConvReLUNorm object from inherited base classes: torch.nn, adapter_mixins with example: obj = ConvReLUNorm(in_channels, out_channels, kernel_size, dropout, condition_dim, condition_types)