def masked_dense(inputs,
                 units,
                 num_blocks=None,
                 exclusive=False,
                 kernel_initializer=None,
                 reuse=None,
                 name=None,
                 *args,  # pylint: disable=keyword-arg-before-vararg
                 **kwargs):
  """A autoregressively masked dense layer. Analogous to `tf.layers.dense`.

  See [Germain et al. (2015)][1] for detailed explanation.

  Arguments:
    inputs: Tensor input.
    units: Python `int` scalar representing the dimensionality of the output
      space.
    num_blocks: Python `int` scalar representing the number of blocks for the
      MADE masks.
    exclusive: Python `bool` scalar representing whether to zero the diagonal of
      the mask, used for the first layer of a MADE.
    kernel_initializer: Initializer function for the weight matrix.
      If `None` (default), weights are initialized using the
      `tf.glorot_random_initializer`.
    reuse: Python `bool` scalar representing whether to reuse the weights of a
      previous layer by the same name.
    name: Python `str` used to describe ops managed by this function.
    *args: `tf.layers.dense` arguments.
    **kwargs: `tf.layers.dense` keyword arguments.

  Returns:
    Output tensor.

  Raises:
    NotImplementedError: if rightmost dimension of `inputs` is unknown prior to
      graph execution.

  #### References

  [1]: Mathieu Germain, Karol Gregor, Iain Murray, and Hugo Larochelle. MADE:
       Masked Autoencoder for Distribution Estimation. In _International
       Conference on Machine Learning_, 2015. https://arxiv.org/abs/1502.03509
  """
  # TODO(b/67594795): Better support of dynamic shape.
  input_depth = tf.compat.dimension_value(
      tensorshape_util.with_rank_at_least(inputs.shape, 1)[-1])
  if input_depth is None:
    raise NotImplementedError(
        "Rightmost dimension must be known prior to graph execution.")

  mask = _gen_mask(num_blocks, input_depth, units,
                   MASK_EXCLUSIVE if exclusive else MASK_INCLUSIVE).T

  if kernel_initializer is None:
    kernel_initializer = tf.compat.v1.glorot_normal_initializer()

  def masked_initializer(shape, dtype=None, partition_info=None):
    return mask * kernel_initializer(shape, dtype, partition_info)

  with tf.compat.v2.name_scope(name or "masked_dense"):
    layer = tf.compat.v1.layers.Dense(
        units,
        kernel_initializer=masked_initializer,
        kernel_constraint=lambda x: mask * x,
        name=name,
        dtype=dtype_util.base_dtype(inputs.dtype),
        _scope=name,
        _reuse=reuse,
        *args,  # pylint: disable=keyword-arg-before-vararg
        **kwargs)
    return layer.apply(inputs)
A autoregressively masked dense layer. Analogous to `tf.layers.dense`.

  See [Germain et al. (2015)][1] for detailed explanation.

  Arguments:
    inputs: Tensor input.
    units: Python `int` scalar representing the dimensionality of the output
      space.
    num_blocks: Python `int` scalar representing the number of blocks for the
      MADE masks.
    exclusive: Python `bool` scalar representing whether to zero the diagonal of
      the mask, used for the first layer of a MADE.
    kernel_initializer: Initializer function for the weight matrix.
      If `None` (default), weights are initialized using the
      `tf.glorot_random_initializer`.
    reuse: Python `bool` scalar representing whether to reuse the weights of a
      previous layer by the same name.
    name: Python `str` used to describe ops managed by this function.
    *args: `tf.layers.dense` arguments.
    **kwargs: `tf.layers.dense` keyword arguments.

  Returns:
    Output tensor.

  Raises:
    NotImplementedError: if rightmost dimension of `inputs` is unknown prior to
      graph execution.

  #### References

  [1]: Mathieu Germain, Karol Gregor, Iain Murray, and Hugo Larochelle. MADE:
       Masked Autoencoder for Distribution Estimation. In _International
       Conference on Machine Learning_, 2015. https://arxiv.org/abs/1502.03509