Computes attention compoenent (query, key or value).

  Args:
    antecedent: a Tensor with shape [batch, length, channels]
    total_depth: an integer
    filter_width: An integer specifying how wide you want the attention
      component to be.
    padding: One of "VALID", "SAME" or "LEFT". Default is VALID: No padding.
    name: a string specifying scope name.
    vars_3d_num_heads: an optional integer (if we want to use 3d variables)
    layer_collection: A tensorflow_kfac.LayerCollection. Only used by the
      KFAC optimizer. Default is None.

  Returns:
    c : [batch, length, depth] tensor

def compute_attention_component(antecedent,
                                total_depth,
                                filter_width=1,
                                padding="VALID",
                                name="c",
                                vars_3d_num_heads=0,
                                layer_collection=None):
  """Computes attention compoenent (query, key or value).

  Args:
    antecedent: a Tensor with shape [batch, length, channels]
    total_depth: an integer
    filter_width: An integer specifying how wide you want the attention
      component to be.
    padding: One of "VALID", "SAME" or "LEFT". Default is VALID: No padding.
    name: a string specifying scope name.
    vars_3d_num_heads: an optional integer (if we want to use 3d variables)
    layer_collection: A tensorflow_kfac.LayerCollection. Only used by the
      KFAC optimizer. Default is None.

  Returns:
    c : [batch, length, depth] tensor
  """
  if layer_collection is not None:
    if filter_width != 1 or vars_3d_num_heads != 0:
      raise ValueError(
          "KFAC implementation only supports filter_width=1 (actual: {}) and "
          "vars_3d_num_heads=0 (actual: {}).".format(
              filter_width, vars_3d_num_heads))
  if vars_3d_num_heads > 0:
    assert filter_width == 1
    input_depth = antecedent.get_shape().as_list()[-1]
    depth_per_head = total_depth // vars_3d_num_heads
    initializer_stddev = input_depth ** -0.5
    if "q" in name:
      initializer_stddev *= depth_per_head ** -0.5
    var = tf.get_variable(
        name, [input_depth,
               vars_3d_num_heads,
               total_depth // vars_3d_num_heads],
        initializer=tf.random_normal_initializer(stddev=initializer_stddev))
    var = tf.cast(var, antecedent.dtype)
    var = tf.reshape(var, [input_depth, total_depth])
    return tf.tensordot(antecedent, var, axes=1)
  if filter_width == 1:
    return common_layers.dense(
        antecedent, total_depth, use_bias=False, name=name,
        layer_collection=layer_collection)
  else:
    return common_layers.conv1d(
        antecedent, total_depth, filter_width, padding=padding, name=name)