code: def __init__(
      self,
      n_layer,
      n_head,
      d_model,
      d_head,
      d_inner,
      kernel_size,
      dropout,
      dropatt,
      dropemb=0.0,
      pre_lnorm=False,
      n_embed=None,
      d_embed=None,
      padding_idx=0,
      condition_types=[],
  ):
      super(FFTransformerEncoder, self).__init__(
          n_layer,
          n_head,
          d_model,
          d_head,
          d_inner,
          kernel_size,
          dropout,
          dropatt,
          dropemb,
          pre_lnorm,
          condition_types,
      )
  
      self.padding_idx = padding_idx
      self.word_emb = nn.Embedding(
          n_embed, d_embed or d_model, padding_idx=self.padding_idx
      )
  
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 FFTransformerEncoder object from inherited base classes: FFTransformerDecoder with example: obj = FFTransformerEncoder(n_layer, n_head, d_model, d_head, d_inner, kernel_size, dropout, dropatt, dropemb, pre_lnorm, n_embed, d_embed, padding_idx, condition_types)