parameters: (x, axis=0, exclusive=False, reverse=False, name=None)
docstring: 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).
      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`.
docstring_summary: Compute the cumulative sum of the tensor `x` along `axis`.
docstring_tokens: Compute the cumulative sum of the tensor x along axis .
function: def cumsum(x, axis=0, exclusive=False, reverse=False, name=None):
    """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).
      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`.
    """
    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)
function_tokens: def cumsum ( x , axis = 0 , exclusive = False , reverse = False , name = None ) : 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 )