code: def _forward(self, inp, mask, conditioning):
      pos_seq = torch.arange(inp.size(1), device=inp.device).to(
          inp.dtype
      )
      pos_emb = self.pos_emb(pos_seq) * mask
      inp = inp + pos_emb
      inp = self.cond_input(inp, conditioning)
      out = self.drop(inp)
  
      for layer in self.layers:
          out = layer(out, mask=mask, conditioning=conditioning)
  
      # out = self.drop(out)
      return out, mask
  
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 5 for calling the FFTransformerDecoder obj's underlying member method code with example usage: obj._forward(inp, mask, conditioning) and returns: out,  mask