code: def __init__(
      self,
      d_feat: int = 20,
      d_model: int = 64,
      batch_size: int = 2048,
      nhead: int = 2,
      num_layers: int = 2,
      dropout: float = 0,
      n_epochs=100,
      lr=0.0001,
      metric="",
      early_stop=5,
      loss="mse",
      optimizer="adam",
      reg=1e-3,
      n_jobs=10,
      GPU=0,
      seed=None,
      **kwargs,
  ):
      # set hyper-parameters.
      self.d_model = d_model
      self.dropout = dropout
      self.n_epochs = n_epochs
      self.lr = lr
      self.reg = reg
      self.metric = metric
      self.batch_size = batch_size
      self.early_stop = early_stop
      self.optimizer = optimizer.lower()
      self.loss = loss
      self.n_jobs = n_jobs
      self.device = torch.device(
          "cuda:%d" % GPU
          if torch.cuda.is_available() and GPU >= 0
          else "cpu"
      )
      self.seed = seed
      self.logger = get_module_logger("TransformerModel")
      self.logger.info(
          "Naive Transformer:"
          "\nbatch_size : {}"
          "\ndevice : {}".format(self.batch_size, self.device)
      )
  
      if self.seed is not None:
          np.random.seed(self.seed)
          torch.manual_seed(self.seed)
  
      self.model = Transformer(
          d_feat, d_model, nhead, num_layers, dropout, self.device
      )
      if optimizer.lower() == "adam":
          self.train_optimizer = optim.Adam(
              self.model.parameters(), lr=self.lr, weight_decay=self.reg
          )
      elif optimizer.lower() == "gd":
          self.train_optimizer = optim.SGD(
              self.model.parameters(), lr=self.lr, weight_decay=self.reg
          )
      else:
          raise NotImplementedError(
              "optimizer {} is not supported!".format(optimizer)
          )
  
      self.fitted = False
      self.model.to(self.device)
  
imports: from __future__ import division
  from __future__ import print_function
  import numpy
  import pandas
  from typing import Text, Union
  import copy
  import math
  from ...utils import get_or_create_path
  from ...log import get_module_logger
  import torch
  import torch.nn
  import torch.optim
  from ...model.base import Model
  from ...data.dataset import DatasetH
  from ...data.dataset.handler import DataHandlerLP
  from torch.nn.modules.container import ModuleList
label_desc: Use image node_id 1 to create a new LocalformerModel object from inherited base classes: Model with example: obj = LocalformerModel(d_feat, d_model, batch_size, nhead, num_layers, dropout, n_epochs, lr, metric, early_stop, loss, optimizer, reg, n_jobs, GPU, seed)