code: def softmax_cross_entropy_one_hot(logits, labels, weights_fn=None):
    
    with tf.variable_scope("softmax_cross_entropy_one_hot",
                           values=[logits, labels]):
      del weights_fn
      cross_entropy = tf.losses.softmax_cross_entropy(
          onehot_labels=labels, logits=logits)
      return cross_entropy, tf.constant(1.0)
docs: Calculate softmax cross entropy given one-hot labels 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