code: def create_twin_estimator(
      self,
      d_data: int,
      d_feats: int,
      **distinct_configs,
  ):
      kernels = [block.kernel for block in self.blocks]
      discs = [block.disc for block in self.blocks]
      d_value = (
          kernels[0].d_head
          if kernels[0].share_values
          else kernels[0].d_hidden
      )
      d_hidden = kernels[0].d_hidden
      window_size = distinct_configs.get(
          "window_size", self.blocks[0].encoder.window_size
      )
      tie_casts = distinct_configs.get(
          "tie_casts", self.blocks[0].encoder.tie_casts
      )
      estimator_configs = dict(
          horizon=distinct_configs.get("horizon", self.horizon),
          loss_func=distinct_configs.get(
              "loss_func", type(self._loss_func)()
          ),
          normalize_input=distinct_configs.get(
              "normalize_input", self.normalize_input
          ),
          rescale_output=distinct_configs.get(
              "rescale_output", self.rescale_output
          ),
          layerwise_loss=distinct_configs.get(
              "layerwise_loss", self.layerwise_loss
          ),
      )
  
      encoder = EncoderModule(
          d_data,
          d_feats,
          d_hidden,
          d_value,
          *window_size,
          tie_casts=tie_casts,
      )
      decoder = DecoderModule(
          d_data,
          d_hidden,
      )
      if self.tie_layers:
          encoders = [encoder] * self.n_layer
          decoders = [decoder] * self.n_layer
      else:
          encoders = [deepcopy(encoder) for _ in range(self.n_layer)]
          decoders = [deepcopy(decoder) for _ in range(self.n_layer)]
      return type(self)(
          encoders, kernels, decoders, discs, **estimator_configs
      )
  
imports: from typing import Optional, List, Tuple, Union, Iterator, Callable
  from itertools import product, chain
  from copy import deepcopy
  import warnings
  import math
  import torch
  from torch import Tensor, LongTensor, BoolTensor
  from torch import nn
  from torch.nn import Parameter, functional
  from tslib.nn.activations import PositiveSoftplus
  from ..network import AttentionKernel, ExpKernel, RBFKernel, EncoderModule, DecoderModule, AttentionBlock, AdversarialBlock, SimpleDiscriminator
  from .hooks import ZScoreNormalizer, LossFunction, MSELoss, MAELoss
label_desc: Use image node_id 12 for calling the AdversarialEstimator obj's underlying member method code with example usage: obj.create_twin_estimator(d_data, d_feats) and returns: type