Fix take_along_axis import for jax backend

tensorflow_probability/python/stats/sample_stats.py

@@ -21,10 +21,12 @@
 import numpy as np
 import tensorflow.compat.v2 as tf
 
-if JAX_MODE or NUMPY_MODE:
-  numpy_ops = np
+if NUMPY_MODE:
+  take_along_axis = np.take_along_axis
+elif JAX_MODE:
+  from jax.numpy import take_along_axis
 else:
-  from tensorflow.python.ops import numpy_ops
+  from tensorflow.python.ops.numpy_ops import take_along_axis
 
 from tensorflow_probability.python.internal import assert_util
 from tensorflow_probability.python.internal import distribution_util
@@ -800,10 +802,10 @@ def windowed_variance(
     def index_for_cumulative(indices):
       return tf.maximum(indices - 1, 0)
     cum_sums = tf.cumsum(x, axis=axis)
-    sums = numpy_ops.take_along_axis(
+    sums = take_along_axis(
         cum_sums, index_for_cumulative(indices), axis=axis)
     cum_variances = cumulative_variance(x, sample_axis=axis)
-    variances = numpy_ops.take_along_axis(
+    variances = take_along_axis(
         cum_variances, index_for_cumulative(indices), axis=axis)
 
     # This formula is the binary accurate variance merge from [1],
@@ -904,8 +906,7 @@ def windowed_mean(
     paddings = ps.reshape(ps.one_hot(2*axis, depth=2*rank, dtype=tf.int32),
                           (rank, 2))
     cum_sums = ps.pad(raw_cumsum, paddings)
-    sums = numpy_ops.take_along_axis(cum_sums, indices,
-      axis=axis)
+    sums = take_along_axis(cum_sums, indices, axis=axis)
     counts = ps.cast(indices[1] - indices[0], dtype=sums.dtype)
     return tf.math.divide_no_nan(sums[1] - sums[0], counts)