code: def forward(self, signal, conditioning=None):
      out = torch.nn.functional.relu(self.conv(signal))
      out = self.norm(out.transpose(1, 2), conditioning).transpose(1, 2)
      out = self.dropout(out)
  
      if self.is_adapter_available():
          out = self.forward_enabled_adapters(
              out.transpose(1, 2)
          ).transpose(1, 2)
  
      return out
  
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 2 for calling the ConvReLUNorm obj's underlying member method code with example usage: obj.forward(signal, conditioning) and returns: out