code: def upconv(x, n, filters):
      x = keras.layers.Conv2D(
          filters=filters,
          kernel_size=1,
          strides=1,
          name=f"upconv{n}.conv.0",
      )(x)
      x = keras.layers.BatchNormalization(
          epsilon=1e-5, momentum=0.9, name=f"upconv{n}.conv.1"
      )(x)
      x = keras.layers.Activation("relu", name=f"upconv{n}.conv.2")(x)
      x = keras.layers.Conv2D(
          filters=filters // 2,
          kernel_size=3,
          strides=1,
          padding="same",
          name=f"upconv{n}.conv.3",
      )(x)
      x = keras.layers.BatchNormalization(
          epsilon=1e-5, momentum=0.9, name=f"upconv{n}.conv.4"
      )(x)
      x = keras.layers.Activation("relu", name=f"upconv{n}.conv.5")(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 4 for calling a global function with example usage: upconv(x, n, filters) and returns: x