def METHOD_NAME(filters, downsample):
    """A default block which serves as an example of the block interface.
    This is the base block definition for a CenterPillar model.
    """
    def apply(x):
        input_depth = list(x.shape)[-1]
        stride = 2 if downsample else 1
        residual = x
        x = keras.layers.Conv2D(
            filters,
            3,
            stride,
            padding="same",
            use_bias=False,
            kernel_initializer=keras.initializers.VarianceScaling(),
            kernel_regularizer=keras.regularizers.L2(l2=1e-4),
        )(x)
        x = keras.layers.BatchNormalization()(x)
        x = keras.layers.ReLU()(x)
        x = keras.layers.Conv2D(
            filters,
            3,
            1,
            padding="same",
            use_bias=False,
            kernel_initializer=keras.initializers.VarianceScaling(),
            kernel_regularizer=keras.regularizers.L2(l2=1e-4),
        )(x)
        x = keras.layers.BatchNormalization()(x)
        x = keras.layers.ReLU()(x)
        if downsample:
            residual = keras.layers.MaxPool2D(
                pool_size=2, strides=2, padding="same"
            )(residual)
        if input_depth != filters:
            residual = keras.layers.Conv2D(
                filters,
                1,
                1,
                padding="same",
                use_bias=False,
                kernel_initializer=keras.initializers.VarianceScaling(),
                kernel_regularizer=keras.regularizers.L2(l2=1e-4),
            )(residual)
            residual = keras.layers.BatchNormalization()(residual)
            residual = keras.layers.ReLU()(residual)
        x = keras.layers.Add()([x, residual])
        return x
    return apply