@tf_export('math.cumsum', 'cumsum')
def cumsum(x, axis=0, exclusive=False, reverse=False, name=None):
    'Compute the cumulative sum of the tensor `x` along `axis`.\n\n  By default, this op performs an inclusive cumsum, which means that the first\n  element of the input is identical to the first element of the output:\n\n  ```python\n  tf.cumsum([a, b, c])  # [a, a + b, a + b + c]\n  ```\n\n  By setting the `exclusive` kwarg to `True`, an exclusive cumsum is performed\n  instead:\n\n  ```python\n  tf.cumsum([a, b, c], exclusive=True)  # [0, a, a + b]\n  ```\n\n  By setting the `reverse` kwarg to `True`, the cumsum is performed in the\n  opposite direction:\n\n  ```python\n  tf.cumsum([a, b, c], reverse=True)  # [a + b + c, b + c, c]\n  ```\n\n  This is more efficient than using separate `tf.reverse` ops.\n\n  The `reverse` and `exclusive` kwargs can also be combined:\n\n  ```python\n  tf.cumsum([a, b, c], exclusive=True, reverse=True)  # [b + c, c, 0]\n  ```\n\n  Args:\n    x: A `Tensor`. Must be one of the following types: `float32`, `float64`,\n       `int64`, `int32`, `uint8`, `uint16`, `int16`, `int8`, `complex64`,\n       `complex128`, `qint8`, `quint8`, `qint32`, `half`.\n    axis: A `Tensor` of type `int32` (default: 0). Must be in the range\n      `[-rank(x), rank(x))`.\n    exclusive: If `True`, perform exclusive cumsum.\n    reverse: A `bool` (default: False).\n    name: A name for the operation (optional).\n\n  Returns:\n    A `Tensor`. Has the same type as `x`.\n  '
    with ops.name_scope(name, 'Cumsum', [x]) as name:
        x = ops.convert_to_tensor(x, name='x')
        return gen_math_ops.cumsum(x, axis, exclusive=exclusive, reverse=reverse, name=name)
Compute the cumulative sum of the tensor `x` along `axis`.

By default, this op performs an inclusive cumsum, which means that the first
element of the input is identical to the first element of the output:

```python
tf.cumsum([a, b, c])  # [a, a + b, a + b + c]
```

By setting the `exclusive` kwarg to `True`, an exclusive cumsum is performed
instead:

```python
tf.cumsum([a, b, c], exclusive=True)  # [0, a, a + b]
```

By setting the `reverse` kwarg to `True`, the cumsum is performed in the
opposite direction:

```python
tf.cumsum([a, b, c], reverse=True)  # [a + b + c, b + c, c]
```

This is more efficient than using separate `tf.reverse` ops.

The `reverse` and `exclusive` kwargs can also be combined:

```python
tf.cumsum([a, b, c], exclusive=True, reverse=True)  # [b + c, c, 0]
```

Args:
  x: A `Tensor`. Must be one of the following types: `float32`, `float64`,
     `int64`, `int32`, `uint8`, `uint16`, `int16`, `int8`, `complex64`,
     `complex128`, `qint8`, `quint8`, `qint32`, `half`.
  axis: A `Tensor` of type `int32` (default: 0). Must be in the range
    `[-rank(x), rank(x))`.
  exclusive: If `True`, perform exclusive cumsum.
  reverse: A `bool` (default: False).
  name: A name for the operation (optional).

Returns:
  A `Tensor`. Has the same type as `x`.
@tf_export('math.cumsum', 'cumsum')
def cumsum(x, axis=0, exclusive=False, reverse=False, name=None):
    'Compute the cumulative sum of the tensor `x` along `axis`.\n\n  By default, this op performs an inclusive cumsum, which means that the first\n  element of the input is identical to the first element of the output:\n\n  ```python\n  tf.cumsum([a, b, c])  # [a, a + b, a + b + c]\n  ```\n\n  By setting the `exclusive` kwarg to `True`, an exclusive cumsum is performed\n  instead:\n\n  ```python\n  tf.cumsum([a, b, c], exclusive=True)  # [0, a, a + b]\n  ```\n\n  By setting the `reverse` kwarg to `True`, the cumsum is performed in the\n  opposite direction:\n\n  ```python\n  tf.cumsum([a, b, c], reverse=True)  # [a + b + c, b + c, c]\n  ```\n\n  This is more efficient than using separate `tf.reverse` ops.\n\n  The `reverse` and `exclusive` kwargs can also be combined:\n\n  ```python\n  tf.cumsum([a, b, c], exclusive=True, reverse=True)  # [b + c, c, 0]\n  ```\n\n  Args:\n    x: A `Tensor`. Must be one of the following types: `float32`, `float64`,\n       `int64`, `int32`, `uint8`, `uint16`, `int16`, `int8`, `complex64`,\n       `complex128`, `qint8`, `quint8`, `qint32`, `half`.\n    axis: A `Tensor` of type `int32` (default: 0). Must be in the range\n      `[-rank(x), rank(x))`.\n    exclusive: If `True`, perform exclusive cumsum.\n    reverse: A `bool` (default: False).\n    name: A name for the operation (optional).\n\n  Returns:\n    A `Tensor`. Has the same type as `x`.\n  '
    with ops.name_scope(name, 'Cumsum', [x]) as name:
        x = ops.convert_to_tensor(x, name='x')
        return gen_math_ops.cumsum(x, axis, exclusive=exclusive, reverse=reverse, name=name)