code: def resize_and_normalize(image, height, width):
      """Convert image to float, resize and normalize to zero mean and [-1, 1]."""
      if image.dtype != tf.float32:
          # Rescale pixel values to float in interval [0.0, 1.0].
          image = tf.image.convert_image_dtype(image, dtype=tf.float32)
  
      # Resize the image to the specified height and width.
      image = tf.expand_dims(image, 0)
      image = tf.image.resize_bilinear(
          image, [height, width], align_corners=False
      )
      image = tf.squeeze(image, [0])
      # Rescale pixels to range [-0.5, 0.5].
      image = tf.subtract(image, 0.5)
      # Rescale pixels to range [-1, 1].
      image = tf.multiply(image, 2.0)
      return image
  
file_path: adanet/research/improve_nas/trainer/image_processing.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import tensorflow.compat.v1
label_desc: Use image node_id 2 for calling a global function with example usage: resize_and_normalize(image, height, width) and returns: image