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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 _my_top_k(x, k):
 """GPU-compatible version of top-k that works for very small constant k. Calls argmax repeatedly. tf.nn.top_k is implemented for GPU, but the gradient, sparse_to_dense, seems not to be, so if we use tf.nn.top_k, then both the top_k and its gradient go on cpu. Once this is not an issue, this function becomes obsolete and should be replaced by tf.nn.top_k. Args: x: a 2d Tensor. k: a small integer. Returns: values: a Tensor of shape [batch_size, k] indices: a int32 Tensor of shape [batch_size, k] """

  if k > 10:
    return tf.nn.top_k(x, k)
  values = []
  indices = []
  depth = tf.shape(x)[1]
  for i in range(k):
    values.append(tf.reduce_max(x, 1))
    argmax = tf.argmax(x, 1)
    indices.append(argmax)
    if i + 1 < k:
      x += tf.one_hot(argmax, depth, -1e9)
  return tf.stack(values, axis=1), tf.to_int32(tf.stack(indices, axis=1))