class_docstr: Estimator class for PyTorch models.
class_docstr_tok: ["Estimator","class","for","PyTorch","models","."]
docstr: Return the type of device on which the estimator is run.
  
  :return: Type of device on which the estimator is run, either `gpu` or `cpu`.
docstr_tok: ["Return","the","type","of","device","on","which","the","estimator","is","run",".",":","return",":","Type","of","device","on","which","the","estimator","is","run",",","either","`","gpu","`","or","`","cpu","`","."]
code: def device_type(self) -> str:
      """
      Return the type of device on which the estimator is run.
  
      :return: Type of device on which the estimator is run, either `gpu` or `cpu`.
      """
      return self._device_type
  
file_path: adversarial-robustness-toolbox/art/estimators/pytorch.py
imports: import logging
  from typing import TYPE_CHECKING, Any, List, Tuple
  import numpy
  from art.estimators.estimator import BaseEstimator, LossGradientsMixin, NeuralNetworkMixin
label_desc: Use image node_id 2 for calling the PyTorchEstimator obj's underlying member method code with example usage: obj.device_type() and returns: self