code: def _reduce_variance(x, axis=None, biased=True, keepdims=False):
      with tf.name_scope("reduce_variance"):
          x = tf.convert_to_tensor(x, name="x")
          mean = tf.reduce_mean(x, axis=axis, keepdims=True)
          biased_var = tf.reduce_mean(
              tf.math.squared_difference(x, mean),
              axis=axis,
              keepdims=keepdims,
          )
          if biased:
              return biased_var
          n = _axis_size(x, axis)
          return (n / (n - 1.0)) * biased_var
  
file_path: probability/tensorflow_probability/python/mcmc/diagnostic.py
imports: import numpy
  import tensorflow.compat.v2
  from tensorflow_probability.python import stats
  from tensorflow_probability.python.internal import assert_util
  from tensorflow_probability.python.internal import dtype_util
  from tensorflow_probability.python.internal import nest_util
  from tensorflow_probability.python.internal import prefer_static
  from tensorflow_probability.python.internal import tensorshape_util
  from tensorflow.python.util import nest
label_desc: Use image node_id 5 for calling a global function with example usage: _reduce_variance(x, axis, biased, keepdims) and returns: unknown, biased_var