class_docstr: PyTorch neural network models for classification/regression with tabular data.
class_docstr_tok: ["PyTorch","neural","network","models","for","classification\/regression","with","tabular","data","."]
code: def _get_device(self, num_gpus):
      import torch
  
      if num_gpus is not None and num_gpus >= 1:
          if torch.cuda.is_available():
              device = torch.device("cuda")
              logger.log(15, "Training on GPU")
              if num_gpus > 1:
                  logger.warning(
                      f"{self.__class__.__name__} not yet able to use more than 1 GPU. 'num_gpus' is set to >1, but we will be using only 1 GPU."
                  )
          else:
              device = torch.device("cpu")
              logger.log(15, "Training on CPU")
      else:
          device = torch.device("cpu")
          logger.log(15, "Training on CPU")
      return device
  
imports: import json
  import logging
  import os
  import random
  import time
  import warnings
  from typing import Dict, Union
  import numpy
  import pandas
  from autogluon.common.features.types import R_BOOL, R_CATEGORY, R_FLOAT, R_INT, S_TEXT_AS_CATEGORY, S_TEXT_NGRAM
  from autogluon.common.utils.pandas_utils import get_approximate_df_mem_usage
  from autogluon.common.utils.resource_utils import ResourceManager
  from autogluon.common.utils.try_import import try_import_torch
  from autogluon.core.constants import BINARY, MULTICLASS, QUANTILE, REGRESSION, SOFTCLASS
  from autogluon.core.hpo.constants import RAY_BACKEND
  from autogluon.core.models.abstract.abstract_nn_model import AbstractNeuralNetworkModel
  from autogluon.core.utils.exceptions import TimeLimitExceeded
  from ..compilers.native import TabularNeuralNetTorchNativeCompiler
  from ..compilers.onnx import TabularNeuralNetTorchOnnxCompiler
  from ..hyperparameters.parameters import get_default_param
  from ..hyperparameters.searchspaces import get_default_searchspace
  from ..utils.data_preprocessor import create_preprocessor, get_feature_arraycol_map, get_feature_type_map
  from ..utils.nn_architecture_utils import infer_y_range
label_desc: Use image node_id 6 for calling the TabularNeuralNetTorchModel obj's underlying member method code with example usage: obj._get_device(num_gpus) and returns: device