class_docstr: GATs Model
  
  Parameters
  ----------
  lr : float
      learning rate
  d_feat : int
      input dimensions for each time step
  metric : str
      the evaluation metric used in early stop
  optimizer : str
      optimizer name
  GPU : int
      the GPU ID used for training
code: def train_epoch(self, data_loader):
      self.GAT_model.train()
  
      for data in data_loader:
          data = data.squeeze()
          feature = data[:, :, 0:-1].to(self.device)
          label = data[:, -1, -1].to(self.device)
  
          pred = self.GAT_model(feature.float())
          loss = self.loss_fn(pred, label)
  
          self.train_optimizer.zero_grad()
          loss.backward()
          torch.nn.utils.clip_grad_value_(
              self.GAT_model.parameters(), 3.0
          )
          self.train_optimizer.step()
  
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 7 for calling the GATs obj's underlying member method code with example usage: obj.train_epoch(data_loader) without return types