code: def _checkKLApprox(self, df, mu, sigma, samples):
      n = samples.size
      np.random.seed(137)
      if not stats:
          return
      sample_scipy = stats.t.rvs(df, loc=mu, scale=sigma, size=n)
      covg = 0.99
      r = stats.t.interval(covg, df, loc=mu, scale=sigma)
      bins = 100
      hist, _ = np.histogram(samples, bins=bins, range=r)
      hist_scipy, _ = np.histogram(sample_scipy, bins=bins, range=r)
      self.assertGreater(hist.sum(), n * (covg - 0.01))
      self.assertGreater(hist_scipy.sum(), n * (covg - 0.01))
      hist_min1 = hist + 1.0  # put at least one item in each bucket
      hist_norm = hist_min1 / hist_min1.sum()
      hist_scipy_min1 = (
          hist_scipy + 1.0
      )  # put at least one item in each bucket
      hist_scipy_norm = hist_scipy_min1 / hist_scipy_min1.sum()
      kl_appx = np.sum(
          np.log(hist_scipy_norm / hist_norm) * hist_scipy_norm
      )
      self.assertLess(kl_appx, 1)
  
imports: import importlib
  import math
  import numpy
  from tensorflow.python.eager import backprop
  from tensorflow.python.framework import constant_op
  from tensorflow.python.framework import random_seed
  from tensorflow.python.framework import test_util
  from tensorflow.python.ops import math_ops
  from tensorflow.python.ops import nn_ops
  from tensorflow.python.ops.distributions import student_t
  from tensorflow.python.platform import test
  from tensorflow.python.platform import tf_logging
label_desc: Use image node_id 9 for calling the StudentTTest obj's underlying member method code with example usage: obj._checkKLApprox(df, mu, sigma, samples) without return types