code: def make_vgg_block(x, filters, n, prefix, pooling=True):
      x = keras.layers.Conv2D(
          filters=filters,
          strides=(1, 1),
          kernel_size=(3, 3),
          padding="same",
          name=f"{prefix}.{n}",
      )(x)
      x = keras.layers.BatchNormalization(
          momentum=0.1, epsilon=1e-5, axis=-1, name=f"{prefix}.{n+1}"
      )(x)
      x = keras.layers.Activation("relu", name=f"{prefix}.{n+2}")(x)
      if pooling:
          x = keras.layers.MaxPooling2D(
              pool_size=(2, 2),
              padding="valid",
              strides=(2, 2),
              name=f"{prefix}.{n+3}",
          )(x)
      return x
  
imports: import typing
  import cv2
  import numpy
  import tensorflow
  import efficientnet.tfkeras
  from tensorflow import keras
  from .None import tools
label_desc: Use image node_id 5 for calling a global function with example usage: make_vgg_block(x, filters, n, prefix, pooling) and returns: x