sxjscience commented on a change in pull request #9747: Add 

 File path: python/mxnet/ndarray/
 @@ -18,9 +18,76 @@
 # coding: utf-8
 # pylint: disable=wildcard-import, unused-wildcard-import
 """Contrib NDArray API of MXNet."""
+import math
+from ..context import current_context
+from ..random import uniform
     from .gen_contrib import *
 except ImportError:
-__all__ = []
+__all__ = ["rand_log_uniform"]
+def rand_log_uniform(true_classes, num_sampled, range_max, ctx=None):
+    """Draw random samples from an approximately log-uniform or Zipfian 
+    This operation randomly samples *num_sampled* candidates the range of 
integers [0, range_max).
+    The elements of sampled_candidates are drawn with replacement from the 
base distribution.
+    The base distribution for this operator is an approximately log-uniform or 
Zipfian distribution:
+    P(class) = (log(class + 2) - log(class + 1)) / log(range_max + 1)
+    This sampler is useful when the true classes approximately follow such a 
+    For example, if the classes represent words in a lexicon sorted in 
decreasing order of \
+    frequency. If your classes are not ordered by decreasing frequency, do not 
use this op.
+    Additionaly, it also returns the number of times each of the \
+    true classes and the sampled classes is expected to occur.
+    Parameters
+    ----------
+    true_classes : NDArray
+        A 1-D NDArray of the target classes.
+    num_sampled: int
+        The number of classes to randomly sample.
+    range_max: int
+        The number of possible classes.
+    ctx : Context
+        Device context of output. Default is current context. Overridden by
+        `mu.context` when `mu` is an NDArray.
+    Returns
+    -------
+    list of NDArrays
+        A 1-D `int64` `NDArray` for sampled candidate classes, a 1-D `float64` 
`NDArray` for \
+        the expected count for true classes, and a 1-D `float64` `NDArray` for 
the \
+        expected count for sampled classes.
 Review comment:
   We need to write the docstring as:
   samples : NDArray
       A 1-D `int64` `NDArray` for sampled candidate classes
   exp_count_true : NDArray
   exp_count_sample : NDArray

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