class_docstr: This class implements a regressor with the PyTorch framework.
class_docstr_tok: ["This","class","implements","a","regressor","with","the","PyTorch","framework","."]
docstr: Compute all loss components.
  
  :param x: Sample input with shape as expected by the model.
  :param y: Target values (class labels) one-hot-encoded of shape `(nb_samples, nb_classes)` or indices
            of shape `(nb_samples,)`.
  :param reduction: Specifies the reduction to apply to the output: 'none' | 'mean' | 'sum'.
             'none': no reduction will be applied
             'mean': the sum of the output will be divided by the number of elements in the output,
             'sum': the output will be summed.
  :return: Dictionary of loss components.
docstr_tok: ["Compute","all","loss","components",".",":","param","x",":","Sample","input","with","shape","as","expected","by","the","model",".",":","param","y",":","Target","values","(","class","labels",")","one-hot-encoded","of","shape","`","(","nb_samples",",","nb_classes",")","`","or","indices","of","shape","`","(","nb_samples",",",")","`",".",":","param","reduction",":","Specifies","the","reduction","to","apply","to","the","output",":","'none","'","|","'mean","'","|","'sum","'",".","'none","'",":","no","reduction","will","be","applied","'mean","'",":","the","sum","of","the","output","will","be","divided","by","the","number","of","elements","in","the","output",",","'sum","'",":","the","output","will","be","summed",".",":","return",":","Dictionary","of","loss","components","."]
code: def compute_losses(
      self,
      x: Union[np.ndarray, "torch.Tensor"],
      y: Union[np.ndarray, "torch.Tensor"],
      reduction: str = "none",
  ) -> Dict[str, Union[np.ndarray, "torch.Tensor"]]:
      """
      Compute all loss components.
  
      :param x: Sample input with shape as expected by the model.
      :param y: Target values (class labels) one-hot-encoded of shape `(nb_samples, nb_classes)` or indices
                of shape `(nb_samples,)`.
      :param reduction: Specifies the reduction to apply to the output: 'none' | 'mean' | 'sum'.
                 'none': no reduction will be applied
                 'mean': the sum of the output will be divided by the number of elements in the output,
                 'sum': the output will be summed.
      :return: Dictionary of loss components.
      """
      return {"total": self.compute_loss(x=x, y=y, reduction=reduction)}
  
imports: from __future__ import absolute_import, division, print_function, unicode_literals
  import copy
  import logging
  import os
  import time
  from typing import Any, Dict, List, Optional, Tuple, Union, TYPE_CHECKING
  import numpy
  import six
  from art import config
  from art.estimators.regression.regressor import RegressorMixin
  from art.estimators.pytorch import PyTorchEstimator
label_desc: Use image node_id 18 for calling the PyTorchRegressor obj's underlying member method code with example usage: obj.compute_losses(x, y, reduction) and returns: dict