code: def __init__(
      self,
      in_dim,
      out_dim=1,
      n_layers=6,
      n_head=1,
      d_head=64,
      d_inner=1024,
      kernel_size=3,
      dropout=0.1,
      dropatt=0.1,
      dropemb=0.0,
  ):
      super(FFTransformer, self).__init__()
      self.in_dim = in_dim
      self.out_dim = out_dim
      self.n_head = n_head
      self.d_head = d_head
  
      self.pos_emb = PositionalEmbedding(self.in_dim)
      self.drop = nn.Dropout(dropemb)
      self.layers = nn.ModuleList()
  
      for _ in range(n_layers):
          self.layers.append(
              TransformerLayer(
                  n_head,
                  in_dim,
                  d_head,
                  d_inner,
                  kernel_size,
                  dropout,
                  dropatt=dropatt,
              )
          )
  
      self.dense = LinearNorm(in_dim, out_dim)
  
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 FFTransformer object from inherited base classes: nn with example: obj = FFTransformer(in_dim, out_dim, n_layers, n_head, d_head, d_inner, kernel_size, dropout, dropatt, dropemb)