code: def __init__(
      self,
      d_feat=6,
      d_model=8,
      nhead=4,
      num_layers=2,
      dropout=0.5,
      device=None,
  ):
      super(Transformer, self).__init__()
      self.feature_layer = nn.Linear(d_feat, d_model)
      self.pos_encoder = PositionalEncoding(d_model)
      self.encoder_layer = nn.TransformerEncoderLayer(
          d_model=d_model, nhead=nhead, dropout=dropout
      )
      self.transformer_encoder = nn.TransformerEncoder(
          self.encoder_layer, num_layers=num_layers
      )
      self.decoder_layer = nn.Linear(d_model, 1)
      self.device = device
      self.d_feat = d_feat
  
imports: from __future__ import division
  from __future__ import print_function
  import numpy
  import pandas
  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 torch.utils.data import DataLoader
  from ...model.base import Model
  from ...data.dataset import DatasetH
  from ...data.dataset.handler import DataHandlerLP
label_desc: Use image node_id 1 to create a new Transformer object from inherited base classes: nn with example: obj = Transformer(d_feat, d_model, nhead, num_layers, dropout, device)