code: def input_types(self):
      return {
          "input": NeuralType(("B", "T"), TokenIndex()),
          "conditioning": NeuralType(
              ("B", "T", "D"), EncodedRepresentation(), optional=True
          ),
      }
  
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 2 for calling the FFTransformerEncoder obj's underlying member method code with example usage: obj.input_types() and returns: dict