code: def distance_loss_corr(x, z_x):
      """Loss based on the distance between elements in a batch"""
  
      # flatten data
      x = tf.keras.layers.Flatten()(x)
      z_x = tf.keras.layers.Flatten()(z_x)
  
      ## z score data
      def z_score(x):
          return (x - tf.reduce_mean(x)) / tf.math.reduce_std(x)
  
      x = z_score(x)
      z_x = z_score(z_x)
  
      # clip distances to 10 standard deviations for stability
      x = tf.clip_by_value(x, -10, 10)
      z_x = tf.clip_by_value(z_x, -10, 10)
  
      dx = tf.math.reduce_euclidean_norm(x[1:] - x[:-1], axis=1)
      dz = tf.math.reduce_euclidean_norm(z_x[1:] - z_x[:-1], axis=1)
  
      # jitter dz to prevent mode collapse
      dz = dz + tf.random.uniform(dz.shape) * 1e-10
  
      # compute correlation
      corr_d = tf.squeeze(
          tensorflow_probability.stats.correlation(
              x=tf.expand_dims(dx, -1), y=tf.expand_dims(dz, -1)
          )
      )
      if tf.math.is_nan(corr_d):
          raise ValueError("NaN values found in correlation loss.")
  
      return -corr_d
  
imports: import numpy
  from umap import UMAP
  from warnings import warn, catch_warnings, filterwarnings
  from numba import TypingError
  import os
  from umap.spectral import spectral_layout
  from sklearn.utils import check_random_state
  import codecs, pickle
  from sklearn.neighbors import KDTree
  import sys
label_desc: Use image node_id 6 for calling a global function with example usage: distance_loss_corr(x, z_x) and returns: unknown, unknown