class_docstr: This module implements the task- and model-specific estimator for PyTorch GOTURN (object tracking).
class_docstr_tok: ["This","module","implements","the","task-","and","model-specific","estimator","for","PyTorch","GOTURN","(","object","tracking",")","."]
docstr: Compute losses.
  
  :param x: Samples of shape (nb_samples, nb_frames, height, width, nb_channels).
  :param y: Target values of format `List[Dict[str, np.ndarray]]`, one dictionary for each input image. The keys
            of the dictionary are:
  
            - boxes [N_FRAMES, 4]: the boxes in [x1, y1, x2, y2] format, with 0 <= x1 < x2 <= W and
                                   0 <= y1 < y2 <= H.
  :return: Dictionary of loss components.
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code: def compute_losses(
      self,
      x: np.ndarray,
      y: List[Dict[str, Union[np.ndarray, "torch.Tensor"]]],
  ) -> Dict[str, np.ndarray]:
      """
      Compute losses.
  
      :param x: Samples of shape (nb_samples, nb_frames, height, width, nb_channels).
      :param y: Target values of format `List[Dict[str, np.ndarray]]`, one dictionary for each input image. The keys
                of the dictionary are:
  
                - boxes [N_FRAMES, 4]: the boxes in [x1, y1, x2, y2] format, with 0 <= x1 < x2 <= W and
                                       0 <= y1 < y2 <= H.
      :return: Dictionary of loss components.
      """
      output = self.compute_loss(x=x, y=y)
      output_dict = {}
      output_dict["torch.nn.L1Loss"] = output
      return output_dict
  
imports: import logging
  import time
  from typing import List, Dict, Optional, Tuple, Union, TYPE_CHECKING
  import numpy
  from art.estimators.object_tracking.object_tracker import ObjectTrackerMixin
  from art.estimators.pytorch import PyTorchEstimator
label_desc: Use image node_id 13 for calling the PyTorchGoturn obj's underlying member method code with example usage: obj.compute_losses(x, y) and returns: output_dict