[T.F 2.0 API Doc] Example for bin_count under tf.math

tensorflow/python/ops/math_ops.py

@@ -3034,6 +3034,26 @@ def bincount(arr,
   value in `weights` at each index where the corresponding value in `arr` is
   `i`.
 
+  ```python
+  values = tf.constant([1,1,2,3,2,4,4,5])
+  tf.math.bincount(values) #[0 2 2 1 2 1]
+  ```
+  Vector length = Maximum element in vector `values` is 5. Adding 1, which is 6
+                  will be the vector length.
+
+  Each bin value in the output indicates number of occurrences of the particular
+  index. Here, index 1 in output has a value 2. This indicates value 1 occurs
+  two times in `values`.
+
+  ```python
+  values = tf.constant([1,1,2,3,2,4,4,5])
+  weights = tf.constant([1,5,0,1,0,5,4,5])
+  tf.math.bincount(values, weights=weights) #[0 6 0 1 9 5]
+  ```
+  Bin will be incremented by the corresponding weight instead of 1.
+  Here, index 1 in output has a value 6. This is the summation of weights
+  corresponding to the value in `values`.
+
   Args:
     arr: An int32 tensor of non-negative values.
     weights: If non-None, must be the same shape as arr. For each value in
@@ -3049,6 +3069,10 @@ def bincount(arr,
   Returns:
     A vector with the same dtype as `weights` or the given `dtype`. The bin
     values.
+
+  Raises:
+    `InvalidArgumentError` if negative values are provided as an input.
+
   """
   name = "bincount" if name is None else name
   with ops.name_scope(name):