code: def __init__(
      self,
      n_speaker_dim,
      bottleneck_hparams,
      take_log_of_input,
      arch_hparams,
      use_transformer=False,
  ):
      super(DAP, self).__init__(take_log_of_input)
      self.bottleneck_layer = BottleneckLayerLayer(**bottleneck_hparams)
      arch_hparams["in_dim"] = (
          self.bottleneck_layer.out_dim + n_speaker_dim
      )
      if use_transformer:
          self.feat_pred_fn = FFTransformer(**arch_hparams)
      else:
          self.feat_pred_fn = ConvLSTMLinear(**arch_hparams)
  
imports: import torch
  import torch.nn
  import torch.nn.functional
  from nemo.collections.tts.modules.common import ConvLSTMLinear
  from nemo.collections.tts.modules.submodules import ConvNorm, MaskedInstanceNorm1d
  from nemo.collections.tts.modules.transformer import FFTransformer
  from nemo.collections.tts.parts.utils.helpers import get_mask_from_lengths
label_desc: Use image node_id 1 to create a new DAP object from inherited base classes: AttributeProcessing with example: obj = DAP(n_speaker_dim, bottleneck_hparams, take_log_of_input, arch_hparams, use_transformer)