code: def darknetConv(
      self,
      filters,
      size,
      strides=1,
      batch_norm=True,
      activate=True,
      activation="leaky",
  ):
      def feed(x):
          if strides == 1:
              padding = "same"
          else:
              x = tf.keras.layers.ZeroPadding2D(((1, 0), (1, 0)))(x)
              padding = "valid"
  
          x = tf.keras.layers.Conv2D(
              filters=filters,
              kernel_size=size,
              strides=strides,
              padding=padding,
              use_bias=not batch_norm,
              kernel_initializer=ScaledRandomUniform(
                  scale=tf.sqrt(2 / (size * size * self.image_size[2])),
                  minval=-0.01,
                  maxval=0.01,
              ),
              kernel_regularizer=tf.keras.regularizers.l2(0.0005),
          )(x)
  
          if batch_norm:
              x = tf.keras.layers.BatchNormalization(
                  moving_variance_initializer="zeros", momentum=0.9
              )(x)
  
          if activate:
              if activation == "mish":
                  x = Mish()(x)
              elif activation == "leaky":
                  x = tf.keras.layers.LeakyReLU(alpha=0.1)(x)
  
          return x
  
      return feed
  
imports: import tensorflow
  import numpy
  from layers import Mish, ScaledRandomUniform
  import utils
label_desc: Use image node_id 8 for calling the YOLOv4Model obj's underlying member method code with example usage: obj.darknetConv(filters, size, strides, batch_norm, activate, activation) and returns: feed, x