code: def sigmoid_cross_entropy_one_hot(logits, labels, weights_fn=None):
    
    with tf.variable_scope("sigmoid_cross_entropy_one_hot",
                           values=[logits, labels]):
      del weights_fn
      cross_entropy = tf.losses.sigmoid_cross_entropy(
          multi_class_labels=labels, logits=logits)
      return cross_entropy, tf.constant(1.0)
docs: Calculate sigmoid cross entropy for one-hot lanels and logits.
  
    Args:
      logits: Tensor of size [batch-size, o=1, p=1, num-classes]
      labels: Tensor of size [batch-size, o=1, p=1, num-classes]
      weights_fn: Function that takes in labels and weighs examples (unused)
    Returns:
      cross_entropy (scalar), weights