code: def get_value_range(
      vmin=-1, vmax=1, in_min=0, in_max=255.0, clip_values=False
  ):
      """Transforms a [in_min,in_max] image to [vmin,vmax] range.
  
      Input ranges in_min/in_max can be equal-size lists to rescale the invidudal
      channels independently.
  
      Args:
        vmin: A scalar. Output max value.
        vmax: A scalar. Output min value.
        in_min: A scalar or a list of input min values to scale. If a list, the
          length should match to the number of channels in the image.
        in_max: A scalar or a list of input max values to scale. If a list, the
          length should match to the number of channels in the image.
        clip_values: Whether to clip the output values to the provided ranges.
  
      Returns:
        A function to rescale the values.
      """
  
      def _value_range(image):
          """Scales values in given range."""
          in_min_t = tf.constant(in_min, tf.float32)
          in_max_t = tf.constant(in_max, tf.float32)
          image = tf.cast(image, tf.float32)
          image = (image - in_min_t) / (in_max_t - in_min_t)
          image = vmin + image * (vmax - vmin)
          if clip_values:
              image = tf.clip_by_value(image, vmin, vmax)
          return image
  
      return _value_range
  
imports: from typing import Optional, Tuple
  import numpy
  from scenic.dataset_lib.big_transfer.preprocessing import autoaugment
  from scenic.dataset_lib.big_transfer.preprocessing import utils
  from scenic.dataset_lib.big_transfer.registry import Registry
  import tensorflow.compat.v1
  import tensorflow.compat.v2
  from tensorflow_addons import image
label_desc: Use image node_id 23 for calling a global function with example usage: get_value_range(vmin, vmax, in_min, in_max, clip_values) and returns: _value_range, image