def METHOD_NAME(
    inputs, pool_size=5, activation="swish", name="spp_fast"
):
    channel_axis = -1
    input_channels = inputs.shape[channel_axis]
    hidden_channels = int(input_channels // 2)
    x = apply_conv_bn(
        inputs,
        hidden_channels,
        kernel_size=1,
        activation=activation,
        name=f"{name}_pre",
    )
    pool_1 = keras.layers.MaxPooling2D(
        pool_size=pool_size, strides=1, padding="same", name=f"{name}_pool1"
    )(x)
    pool_2 = keras.layers.MaxPooling2D(
        pool_size=pool_size, strides=1, padding="same", name=f"{name}_pool2"
    )(pool_1)
    pool_3 = keras.layers.MaxPooling2D(
        pool_size=pool_size, strides=1, padding="same", name=f"{name}_pool3"
    )(pool_2)
    out = ops.concatenate([x, pool_1, pool_2, pool_3], axis=channel_axis)
    out = apply_conv_bn(
        out,
        input_channels,
        kernel_size=1,
        activation=activation,
        name=f"{name}_output",
    )
    return out