code: def from_configs(
      cls,
      d_data: int,
      d_feats: int,
      d_hidden: int,
      n_layer: int,
      horizon: Union[int, float],
      window_size: List[int],
      n_head: int = 4,
      n_enc_layer: int = 2,
      n_dec_layer: int = 2,
      symmetric: bool = False,
      share_values: bool = False,
      tie_casts: bool = False,
      tie_layers: bool = False,
      dropout: float = 0.0,
      temperature: float = 1.0,
      norm: str = "instance",
      add_dist: bool = False,
      normalize_input: bool = True,
      rescale_output: bool = True,
      layerwise_loss: bool = True,
  ):
      kernel = ExpKernel(
          d_hidden,
          n_head,
          n_enc_layer,
          n_dec_layer,
          symmetric=symmetric,
          share_values=share_values,
          dropout=dropout,
          temperature=temperature,
          norm=norm,
          add_dist=add_dist,
      )
      encoder = EncoderModule(
          d_data,
          d_feats,
          d_hidden,
          kernel.d_head if share_values else d_hidden,
          *window_size,
          tie_casts=tie_casts,
      )
      decoder = DecoderModule(d_data, d_hidden)
      if tie_layers:
          encoders = [encoder] * n_layer
          decoders = [decoder] * n_layer
          kernels = [kernel] * n_layer
      else:
          encoders = [deepcopy(encoder) for _ in range(n_layer)]
          decoders = [deepcopy(decoder) for _ in range(n_layer)]
          kernels = [deepcopy(kernel) for _ in range(n_layer)]
  
      return cls(
          encoders,
          kernels,
          decoders,
          horizon=horizon,
          normalize_input=normalize_input,
          rescale_output=rescale_output,
          layerwise_loss=layerwise_loss,
      )
  
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 10 for calling the AttentionEstimator obj's underlying member method code with example usage: obj.from_configs(cls, d_data, d_feats, d_hidden, n_layer, horizon, window_size, n_head, n_enc_layer, n_dec_layer, symmetric, share_values, tie_casts, tie_layers, dropout, temperature, norm, add_dist, normalize_input, rescale_output, layerwise_loss) and returns: cls