class_docstr: The convolutional layers custom to the evolved transformer decoder.
  
  The input is passed through a 11x1 separable convolution followed by a ReLU on
  the left branch while it goes through a 7x1 separable convolution on the right
  branch. The outputs of the branches are summed and then passed through a layer
  norm. The output of the layer norm then goes through separable 7x1
  convolution.
docstr: Create an DecoderConvolutionalLayer.
  
  Args:
    d_model: a positive integer, the dimension of the model dim.
    dropout_rate: a float between 0 and 1.
    initializer_scale: a positive float, the scale for the initializers of the
      separable convolutional filters.
    norm_epsilon: a small positive float, the epsilon for the layer norm.
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code: def __init__(
      self,
      d_model,
      dropout_rate,
      initializer_scale=1.0,
      norm_epsilon=1e-6,
  ):
      """Create an DecoderConvolutionalLayer.
  
      Args:
        d_model: a positive integer, the dimension of the model dim.
        dropout_rate: a float between 0 and 1.
        initializer_scale: a positive float, the scale for the initializers of the
          separable convolutional filters.
        norm_epsilon: a small positive float, the epsilon for the layer norm.
      """
      self._d_model = d_model
      self._dropout_rate = dropout_rate
      self._norm_epsilon = norm_epsilon
      self._sep_conv11x1 = transformer_layers.SeparableConv1DLayer(
          min_relative_pos=-10,
          max_relative_pos=0,
          output_size=int(2 * d_model),
          depthwise_filter_initializer_scale=initializer_scale,
          pointwise_filter_initializer_scale=initializer_scale,
          activation="relu",
      )
      self._sep_conv7x1_1 = transformer_layers.SeparableConv1DLayer(
          min_relative_pos=-6,
          max_relative_pos=0,
          output_size=int(d_model / 2),
          depthwise_filter_initializer_scale=initializer_scale,
          pointwise_filter_initializer_scale=initializer_scale,
      )
      self._sep_conv7x1_2 = transformer_layers.SeparableConv1DLayer(
          min_relative_pos=-6,
          max_relative_pos=0,
          output_size=d_model,
          depthwise_filter_initializer_scale=initializer_scale,
          pointwise_filter_initializer_scale=initializer_scale,
      )
  
imports: import gin
  import mesh_tensorflow
  from mesh_tensorflow.transformer import transformer
  from mesh_tensorflow.transformer import transformer_layers
  import tensorflow.compat.v1
label_desc: Use image node_id 1 to create a new DecoderConvolutionalLayer object from inherited base classes: transformer with example: obj = DecoderConvolutionalLayer(d_model, dropout_rate, initializer_scale, norm_epsilon)