code: def init(
      images,
      num_channels,
      dim="2d",
      stride=2,
      kernel_size=7,
      maxpool=True,
      training=True,
      scope="init",
  ):
      """Standard ResNet initial block used as first RevNet block.
  
      Args:
        images: [N, H, W, 3] tensor of input images to the model.
        num_channels: Output depth of convolutional layer in initial block.
        dim: '2d' if 2-dimensional, '3d' if 3-dimensional.
        stride: stride for the convolution and pool layer.
        kernel_size: Size of the initial convolution filter
        maxpool: If true, apply a maxpool after the convolution
        training: True for train phase, False for eval phase.
        scope: Optional scope for the init block.
  
      Returns:
        Two [N, H, W, C] output activations from input images.
      """
      conv = CONFIG[dim]["conv"]
      pool = CONFIG[dim]["max_pool"]
      with tf.variable_scope(scope):
          net = conv(
              images,
              num_channels,
              kernel_size,
              strides=stride,
              padding="SAME",
              activation=None,
          )
          net = tf.layers.batch_normalization(net, training=training)
          net = tf.nn.relu(net)
          if maxpool:
              net = pool(net, pool_size=3, strides=stride)
          x1, x2 = tf.split(net, 2, axis=CONFIG[dim]["split_axis"])
          return x1, x2
  
imports: import functools
  from tensor2tensor.layers import common_hparams
  from tensor2tensor.utils import contrib
  from tensor2tensor.utils import registry
  from tensor2tensor.utils import t2t_model
  import tensorflow.compat.v1
  from tensorflow.compat.v1 import estimator
label_desc: Use image node_id 5 for calling a global function with example usage: init(images, num_channels, dim, stride, kernel_size, maxpool, training, scope) and returns: x1,  x2