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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 multihead_attention_params(mesh, heads, io_channels, kv_channels, variable_dtype, combine=False):
 """Create Parameters for Multihead Attention. If the combine flag is set to True, then we create only one variable which stacks together all of the parameters. Otherwise, we create four separate variables. Args: mesh: a Mesh heads: a Dimension io_channels: a Dimension kv_channels: a Dimension variable_dtype: a mtf.VariableDType combine: a boolean Returns: wq: a Tensor with shape [heads, io_channels, kv_channels] wk: a Tensor with shape [heads, io_channels, kv_channels] wv: a Tensor with shape [heads, io_channels, kv_channels] wo: a Tensor with shape [heads, io_channels, kv_channels] """

  qkvo = mtf.Dimension("qkvo", 4)
  qk_stddev = (io_channels.size ** -0.5) * (kv_channels.size ** -0.25)
  v_stddev = io_channels.size ** -0.5
  # TODO(noam): should be: o_stddev = (kv_channels.size * heads.size) ** -0.5
  #   verify that this still works and change it.
  o_stddev = (io_channels.size * heads.size) ** -0.5
  if combine:
    def qkvo_initializer(shape,
                         dtype=None,
                         partition_info=None,
                         verify_shape=None):
      del partition_info, verify_shape
      return tf.random_normal(shape, dtype=dtype) * tf.reshape(
          tf.cast([qk_stddev, qk_stddev, v_stddev, o_stddev],
                  dtype or tf.float32), [4, 1, 1, 1])
    var = mtf.get_variable(
        mesh, "qkvo", mtf.Shape([qkvo, heads, io_channels, kv_channels]),
        initializer=qkvo_initializer, dtype=variable_dtype)
    return mtf.unstack(var, qkvo)
  else:
    return [mtf.get_variable(
        mesh, name, mtf.Shape([heads, io_channels, kv_channels]),
        initializer=tf.random_normal_initializer(stddev=stddev),
        dtype=variable_dtype) for name, stddev in zip(
            ["q", "k", "v", "o"],
            [qk_stddev, qk_stddev, v_stddev, o_stddev])]