code: def conv_block(
      x: tf.Tensor,
      channels: int = 64,
      ksize: int = 3,
      padding: str = "same",
      bn: bool = False,
  ) -> tf.Tensor:
      """Constructs a convolution block.
  
      :param x: inputs [batch_size, h, w, nb_channels]
      :param channels: number of channels (int)
      :param ksize: kernel size (int or a tuple of ints)
      :param padding: padding type (str)
      :param bn: whether to use Batch Normalization
      :return: output of the convolution block
      """
      x = layers.Conv2D(
          channels,
          kernel_size=ksize,
          padding=padding,
          use_bias=bn,
          kernel_initializer="orthogonal",
      )(x)
      if bn:
          x = layers.BatchNormalization(momentum=0.0, epsilon=0.0001)(x)
      x = layers.Activation("relu")(x)
      return x
  
imports: from tensorflow.keras import layers
  import tensorflow
label_desc: Use image node_id 1 for calling a global function with example usage: conv_block(x, channels, ksize, padding, bn) and returns: x