code: def ctv_debatched(
      det_bounds, dim, num_samples, error_rate=1e-6, seed=42
  ):
      # This wrapper undoes the batching in compute_true_volumes, because
      # apparently several 5x5x9x1e7 Tensors of float32 can strain RAM.
      bounds = {}
      for db in det_bounds:
          bounds[db] = corr.compute_true_volumes(
              [db], dim, num_samples, error_rate=error_rate, seed=seed
          )[db]
      return bounds
  
file_path: probability/tensorflow_probability/python/distributions/internal/correlation_matrix_volumes.py
imports: import pprint
  from absl import app
  from absl import flags
  from tensorflow_probability.python.distributions.internal import correlation_matrix_volumes_lib
label_desc: Use image node_id 1 for calling a global function with example usage: ctv_debatched(det_bounds, dim, num_samples, error_rate, seed) and returns: bounds