code: def dense_relu_dense(inputs,
                       filter_size,
                       output_size,
                       output_activation=None,
                       dropout=0.0,
                       dropout_broadcast_dims=None,
                       layer_collection=None,
                       name=None):
    
    # layer_name is appended with "conv1" or "conv2" in this method only for
    # historical reasons. These are in fact dense layers.
    layer_name = "%s_{}" % name if name else "{}"
    h = dense(
        inputs,
        filter_size,
        use_bias=True,
        activation=tf.nn.relu,
        layer_collection=layer_collection,
        name=layer_name.format("conv1"))
  
    if dropout != 0.0:
      h = dropout_with_broadcast_dims(
          h, 1.0 - dropout, broadcast_dims=dropout_broadcast_dims)
    o = dense(
        h,
        output_size,
        activation=output_activation,
        use_bias=True,
        layer_collection=layer_collection,
        name=layer_name.format("conv2"))
    return o
docs: Hidden layer with RELU activation followed by linear projection.