A stack of separable convolution blocks with residual connections.

def multi_conv_res(x, padding, name, layers, hparams, mask=None, source=None):
  """A stack of separable convolution blocks with residual connections."""
  with tf.variable_scope(name):
    padding_bias = None
    if mask is not None:
      padding_bias = (1.0 - mask) * -1e9  # Bias to not attend to padding.
      if padding == "LEFT":  # Do not mask anything when left-padding.
        mask = None
    if (hparams.kernel_scheme in _KERNEL_SCHEMES and
        hparams.dilation_scheme in _DILATION_SCHEMES):
      kernels = _KERNEL_SCHEMES[hparams.kernel_scheme]
      dilations = _DILATION_SCHEMES[hparams.dilation_scheme]
      dilations_and_kernels = list(zip(dilations, kernels))
      dilations_and_kernels1 = dilations_and_kernels[:2]
      dilations_and_kernels2 = dilations_and_kernels[2:]
    else:
      k = (hparams.kernel_height, hparams.kernel_width)
      k2 = (hparams.large_kernel_size, 1)
      dilations_and_kernels1 = [((1, 1), k), ((1, 1), k)]
      dilations_and_kernels2 = [((1, 1), k2), ((4, 4), k2)]
    separabilities1 = [hparams.separability, hparams.separability]
    separabilities2 = [hparams.separability] * len(dilations_and_kernels2)
    if hparams.separability < 0:
      separabilities1 = [hparams.separability - 1, hparams.separability]
      separabilities2 = [
          hparams.separability - i
          for i in reversed(range(len(dilations_and_kernels2)))
      ]

    def norm_fn(x, name):
      with tf.variable_scope(name, default_name="norm"):
        return common_layers.apply_norm(
            x, hparams.norm_type, hparams.hidden_size, hparams.norm_epsilon)

    for layer in range(layers):
      with tf.variable_scope("layer_%d" % layer):
        y = common_layers.subseparable_conv_block(
            x,
            hparams.hidden_size,
            dilations_and_kernels1,
            normalizer_fn=norm_fn,
            padding=padding,
            mask=mask,
            separabilities=separabilities1,
            name="residual1")
        x += common_layers.subseparable_conv_block(
            x + y,
            hparams.hidden_size,
            dilations_and_kernels2,
            normalizer_fn=norm_fn,
            padding=padding,
            mask=mask,
            separabilities=separabilities2,
            name="residual2") + y
        if source is not None and hparams.attention_type != "none":
          x += attention(x, source, norm_fn, hparams, bias=padding_bias)
        if mask is not None:
          x *= mask
    return tf.nn.dropout(x, 1.0 - hparams.dropout)