code: def sigmoid_recall_one_hot(logits, labels, weights_fn=None):
    
    with tf.variable_scope("sigmoid_recall_one_hot", values=[logits, labels]):
      del weights_fn
      num_classes = logits.shape[-1]
      predictions = tf.nn.sigmoid(logits)
      predictions = tf.argmax(predictions, -1)
      predictions = tf.one_hot(predictions, num_classes)
      _, recall = tf.metrics.recall(labels=labels, predictions=predictions)
      return recall, tf.constant(1.0)
docs: Calculate recall for a set, given one-hot labels and logits.
  
    Predictions are converted to one-hot,
    as predictions[example][arg-max(example)] = 1
  
    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:
      recall (scalar), weights