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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def multilabel_accuracy_matchk(predictions, labels, k, weights_fn=common_layers.weights_nonzero):
 """Used to evaluate the VQA accuracy. Let n be the times that predictions appear in labels, then final score is min(n/k, 1). Refer to https://arxiv.org/pdf/1505.00468.pdf. Args: predictions: A tensor with shape [batch_size, 1, 1, 1, vocab_size]. labels: A tensor with shape [batch_size, length, 1, 1]. k: A tensor constant. weights_fn: weight function. Returns: scores: min(n/k, 1). weights: returns all ones. """

  predictions = tf.to_int32(tf.argmax(predictions, axis=-1))
  scores = tf.to_float(tf.equal(predictions, labels))
  # those label == 0 do not count
  weights = weights_fn(labels)
  scores *= weights
  scores = tf.reduce_sum(scores, axis=[1, 2, 3])
  scores = tf.minimum(scores / tf.to_float(k), 1)
  # every sample count
  weights = tf.ones(tf.shape(scores), dtype=tf.float32)

  return scores, weights