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: Call the layer.
docstr_tok: ["Call","the","layer","."]
code: def call(self, context, x, losses=None):
      """Call the layer."""
      with tf.variable_scope("sep_conv11x1"):
          left_state = self._sep_conv11x1.call(context, x, losses)
          left_state = _dropout(left_state, context, self._dropout_rate)
  
      with tf.variable_scope("sep_conv7x1_1"):
          right_state = self._sep_conv7x1_1.call(context, x, losses)
          right_state = _dropout(
              right_state, context, self._dropout_rate
          )
          right_state = _pad_channels_dim(
              right_state, left_state.shape.dims[-1].size
          )
  
      hidden_state = left_state + right_state
      hidden_state = mtf.layers.layer_norm(
          hidden_state,
          epsilon=self._norm_epsilon,
          dim=hidden_state.shape.dims[-1],
      )
  
      with tf.variable_scope("sep_conv7x1_2"):
          output = self._sep_conv7x1_2.call(
              context, hidden_state, losses
          )
          return _dropout(output, context, self._dropout_rate)
  
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 2 for calling the DecoderConvolutionalLayer obj's underlying member method code with example usage: obj.call(context, x, losses) and returns: _dropout