code: def __init__(
      self,
      n_head,
      d_model,
      d_head,
      d_inner,
      kernel_size,
      dropout,
      condition_types=[],
      **kwargs,
  ):
      super(TransformerLayer, self).__init__()
  
      self.dec_attn = MultiHeadAttn(
          n_head,
          d_model,
          d_head,
          dropout,
          condition_types=condition_types,
          **kwargs,
      )
      self.pos_ff = PositionwiseConvFF(
          d_model,
          d_inner,
          kernel_size,
          dropout,
          pre_lnorm=kwargs.get("pre_lnorm"),
          condition_types=condition_types,
      )
  
imports: from typing import Optional
  import torch
  import torch.nn
  import torch.nn.functional
  from nemo.collections.tts.modules.submodules import ConditionalInput, ConditionalLayerNorm, LinearNorm
  from nemo.collections.tts.parts.utils.helpers import get_mask_from_lengths
  from nemo.core.classes import NeuralModule, adapter_mixins, typecheck
  from nemo.core.neural_types.elements import EncodedRepresentation, LengthsType, MaskType, TokenIndex
  from nemo.core.neural_types.neural_type import NeuralType
label_desc: Use image node_id 1 to create a new TransformerLayer object from inherited base classes: nn, adapter_mixins with example: obj = TransformerLayer(n_head, d_model, d_head, d_inner, kernel_size, dropout, condition_types)