class_docstr: Computes the Kullback-Liebler divergence metric between labels and
  predictions.
  
  Usage:
  
  ```python
  pred = torch.tensor([[0.6, 0.4], [0.4, 0.6]])
  target = torch.tensor([[0, 1], [0, 0]])
  div = KLDivergence()
  div(pred, target)
  assert abs(div.compute() - 0.45814306) < 1e-6
  ```
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code: def __init__(self):
      self.total = torch.tensor(0)
      self.divergence = torch.tensor(0)
  
imports: import torch
  from abc import ABC, abstractmethod
label_desc: Use image node_id 1 to create a new KLDivergence object from inherited base classes: PytorchMetric with example: obj = KLDivergence()