class_docstr: Computes the crossentropy metric between the labels and predictions.
  This is used when there are only two labels (0 and 1).
  
  Usage:
  
  ```python
  pred = torch.tensor([[0.6, 0.4], [0.4, 0.6]])
  target = torch.tensor([[0, 1], [0, 0]])
  entropy = BinaryCrossEntropy()
  entropy(pred, target)
  assert abs(entropy.compute() - 0.81492424) < 1e-6
  ```
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code: def __init__(self):
      self.total = torch.tensor(0)
      self.crossentropy = torch.tensor(0)
  
imports: import torch
  from abc import ABC, abstractmethod
label_desc: Use image node_id 1 to create a new BinaryCrossEntropy object from inherited base classes: PytorchMetric with example: obj = BinaryCrossEntropy()