code: def uniform_binning_correction(x, n_bits=8):
      """Replaces x^i with q^i(x) = U(x, x + 1.0 / 256.0).
  
      Args:
        x: 4-D Tensor of shape (NHWC)
        n_bits: optional.
      Returns:
        x: x ~ U(x, x + 1.0 / 256)
        objective: Equivalent to -q(x)*log(q(x)).
      """
      n_bins = 2**n_bits
      batch_size, height, width, n_channels = common_layers.shape_list(
          x
      )
      hwc = float(height * width * n_channels)
  
      x = x + tf.random_uniform(
          shape=(batch_size, height, width, n_channels),
          minval=0.0,
          maxval=1.0 / n_bins,
      )
      objective = -np.log(n_bins) * hwc * tf.ones(batch_size)
      return x, objective
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import functools
  import numpy
  import scipy
  from tensor2tensor.layers import common_layers
  from tensor2tensor.layers import common_video
  from tensor2tensor.utils import contrib
  import tensorflow.compat.v1
  from tensorflow.compat.v1 import estimator
  import tensorflow_probability
label_desc: Use image node_id 39 for calling a global function with example usage: uniform_binning_correction(x, n_bits) and returns: x,  objective