code: def log_normal(low, high, q):
      """Samples values from a clipped log-normal distribution quantized to q."""
      # mu/sigma are computed from low/high such that ~99.7% of samples are in range
      log_low, log_high = np.log(low), np.log(high)
      f, mu, sigma = (
          np.inf,
          (log_low + log_high) / 2,
          (log_high - log_low) / 6,
      )
      while not low <= f <= high:
          f = np.random.lognormal(mu, sigma)
      return quantize(f, q, np.round)
  
file_path: pycls/pycls/sweep/random.py
imports: from decimal import Decimal
  import matplotlib.pyplot
  import numpy
label_desc: Use image node_id 7 for calling a global function with example usage: log_normal(low, high, q) and returns: quantize