message: Updated reduce_sum description to include comments in code sample
diff: @@ -1712,20 +1712,41 @@ def reduce_sum(input_tensor, axis=None, keepdims=False, name=None):
  For example:
  - ```python
  - x = tf.constant([[1, 1, 1], [1, 1, 1]])
  - tf.reduce_sum(x) # 6
  - tf.reduce_sum(x, 0) # [2, 2, 2]
  - tf.reduce_sum(x, 1) # [3, 3]
  - tf.reduce_sum(x, 1, keepdims=True) # [[3], [3]]
  - tf.reduce_sum(x, [0, 1]) # 6
  - ```
  + >>> # x has a shape of (2, 3) (two rows and three columns):
  + >>> x = tf.constant([[1, 1, 1], [1, 1, 1]])
  + >>> x.numpy()
  + array([[1, 1, 1],
  + [1, 1, 1]], dtype=int32)
  + >>> # sum all the elements
  + >>> # 1 + 1 + 1 + 1 + 1+ 1 = 6
  + >>> tf.reduce_sum(x).numpy()
  + 6
  + >>> # reduce along the first dimension
  + >>> # the result is [1, 1, 1] + [1, 1, 1] = [2, 2, 2]
  + >>> tf.reduce_sum(x, 0).numpy()
  + array([2, 2, 2], dtype=int32)
  + >>> # reduce along the second dimension
  + >>> # the result is [1, 1] + [1, 1] + [1, 1] = [3, 3]
  + >>> tf.reduce_sum(x, 1).numpy()
  + array([3, 3], dtype=int32)
  + >>> # keep the original dimensions
  + >>> tf.reduce_sum(x, 1, keepdims=True).numpy()
  + array([[3],
  + [3]], dtype=int32)
  + >>> # reduce along both dimensions
  + >>> # the result is 1 + 1 + 1 + 1 + 1 + 1 = 6
  + >>> # or, equivalently, reduce along rows, then reduce the resultant array
  + >>> # [1, 1, 1] + [1, 1, 1] = [2, 2, 2]
  + >>> # 2 + 2 + 2 = 6
  + >>> tf.reduce_sum(x, [0, 1]).numpy()
  + 6
  +
  Args:
  input_tensor: The tensor to reduce. Should have numeric type.
  axis: The dimensions to reduce. If `None` (the default), reduces all
  dimensions. Must be in the range `[-rank(input_tensor),
  - rank(input_tensor))`.
  + rank(input_tensor)]`.
  keepdims: If true, retains reduced dimensions with length 1.
  name: A name for the operation (optional).
  