code: def _linear_decorelate_color(t):
      """Multiply input by sqrt of empirical (ImageNet) color correlation matrix.
  
      If you interpret t's innermost dimension as describing colors in a
      decorrelated version of the color space (which is a very natural way to
      describe colors -- see discussion in Feature Visualization article) the way
      to map back to normal colors is multiply the square root of your color
      correlations.
      """
      # check that inner dimension is 3?
      t_flat = tf.reshape(t, [-1, 3])
      color_correlation_normalized = (
          color_correlation_svd_sqrt / max_norm_svd_sqrt
      )
      t_flat = tf.matmul(t_flat, color_correlation_normalized.T)
      t = tf.reshape(t_flat, tf.shape(t))
      return t
  
imports: import numpy
  import tensorflow
  from lucid.optvis.param.unit_balls import constrain_L_inf
label_desc: Use image node_id 1 for calling a global function with example usage: _linear_decorelate_color(t) and returns: t