code: def __init__(
      self,
      d_feat=6,
      hidden_size=64,
      num_layers=2,
      dropout=0.0,
      base_model="GRU",
  ):
      super().__init__()
  
      if base_model == "GRU":
          self.rnn = nn.GRU(
              input_size=d_feat,
              hidden_size=hidden_size,
              num_layers=num_layers,
              batch_first=True,
              dropout=dropout,
          )
      elif base_model == "LSTM":
          self.rnn = nn.LSTM(
              input_size=d_feat,
              hidden_size=hidden_size,
              num_layers=num_layers,
              batch_first=True,
              dropout=dropout,
          )
      else:
          raise ValueError("unknown base model name `%s`" % base_model)
  
      self.hidden_size = hidden_size
      self.d_feat = d_feat
      self.transformation = nn.Linear(
          self.hidden_size, self.hidden_size
      )
      self.a = nn.Parameter(torch.randn(self.hidden_size * 2, 1))
      self.a.requires_grad = True
      self.fc = nn.Linear(self.hidden_size, self.hidden_size)
      self.fc_out = nn.Linear(hidden_size, 1)
      self.leaky_relu = nn.LeakyReLU()
      self.softmax = nn.Softmax(dim=1)
  
imports: from __future__ import division
  from __future__ import print_function
  import numpy
  import pandas
  import copy
  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 torch.utils.data import Sampler
  from .pytorch_utils import count_parameters
  from ...model.base import Model
  from ...data.dataset.handler import DataHandlerLP
  from ...contrib.model.pytorch_lstm import LSTMModel
  from ...contrib.model.pytorch_gru import GRUModel
label_desc: Use image node_id 1 to create a new GATModel object from inherited base classes: nn with example: obj = GATModel(d_feat, hidden_size, num_layers, dropout, base_model)