docstr: ivy.Container instance method variant of ivy.cumsum. This method
  simply wraps the function, and so the docstring for ivy.cumsum also
  applies to this method with minimal changes.
  
  Parameters
  ----------
  self
      Input container to apply cumsum at leaves.
  axis
      Axis along which the cumulative sum is computed. Default is ``0``.
  exclusive
      Whether to perform cumsum exclusively. Default is ``False``.
  reverse
      Whether to perform the cumsum from last to first element in the selected
      axis. Default is ``False`` (from first to last element)
  key_chains
      The key-chains to apply or not apply the method to. Default is ``None``.
  to_apply
      If True, the method will be applied to key_chains, otherwise key_chains
      will be skipped. Default is ``True``.
  prune_unapplied
      Whether to prune key_chains for which the function was not applied.
      Default is ``False``.
  map_sequences
      Whether to also map method to sequences (lists, tuples).
      Default is ``False``.
  dtype
      Data type of the returned array. Default is ``None``.
  out
      Optional output container. Default is ``None``.
  
  Returns
  -------
  ret
      Container whose leaves hold the result of applying cumsum
      at each original leaf arrays along the specified axis.
  
  Examples
  --------
  With :class:`ivy.Container` input:
  
  >>> x = ivy.Container(a=ivy.array([[1, 2, 3],
  ...                                [2, 4, 5]]),
  ...                   b=ivy.array([[4, 5, 6],
  ...                                [2, 3, 1 ]]))
  >>> y = x.cumsum(axis=0, dtype='float64')
  >>> print(y)
  {
      a: ivy.array([[1., 2., 3.],
                    [3., 6., 8.]]),
      b: ivy.array([[4., 5., 6.],
                    [6., 8., 7.]])
  }
  
  >>> x = ivy.Container(a=ivy.array([[1, 3, 4],
  ...                                [5, 7, 8],
  ...                                [9, 10, 11]]),
  ...                   b=ivy.array([[3, 4, 5],
  ...                                [4, 5, 6],
  ...                                [5, 6, 7]]))
  >>> y = ivy.Container(a= ivy.zeros((3, 3)), b= ivy.zeros((3, 3)))
  >>> x.cumsum(axis=1, exclusive=False, reverse=True, out=y)
  >>> print(y)
  {
      a: ivy.array([[8, 7, 4],
                    [20, 15, 8],
                    [30, 21, 11]]),
      b: ivy.array([[12, 9, 5],
                    [15, 11, 6],
                    [18, 13, 7]])
  }
  
  >>> x = ivy.Container(a=ivy.array([[1, 3, 4]]),
  ...                   b=ivy.array([[3, 5, 8],
  ...                                [5, 6, 5]]),
  ...                   c=ivy.array([[2, 4, 1],
  ...                                [3, 6, 9],
  ...                                [0, 2, 3]]))
  >>> y = ivy.Container(a = ivy.zeros((1, 3)),
  ...                   b = ivy.zeros((2, 3)),
  ...                   c = ivy.zeros((3,3)))
  >>> x.cumsum(axis=1,exclusive=True, reverse=False, out=y)
  >>> print(y)
  {
      a: ivy.array([[0, 1, 4]]),
      b: ivy.array([[0, 3, 8],
                    [0, 5, 11]]),
      c: ivy.array([[0, 2, 6],
                    [0, 3, 9],
                    [0, 0, 2]])
  }
  
  >>> x = ivy.Container(a=ivy.array([[0, 3, 2],
  ...                                [5, 10, 2],
  ...                                [1, 10, 1]]),
  ...                   b=ivy.array([[2, 4, 5],
  ...                                [4, 5, 5],
  ...                                [0, 1, 3]]))
  >>> y = x.cumsum(axis=1,exclusive=True, reverse=True, dtype='int64')
  >>> print(y)
  {
      a: ivy.array([[5, 2, 0],
                    [12, 2, 0],
                    [11, 1, 0]]),
      b: ivy.array([[9, 5, 0],
                    [10, 5, 0],
                    [4, 3, 0]])
  }
  
  >>> x = ivy.Container(a=ivy.array([[0],
  ...                                [5]]),
  ...                   b=ivy.array([[6, 8, 7],
  ...                                [4, 2, 3]]),
  ...                   c=ivy.array([[1, 2],
  ...                                [3, 4],
  ...                                [6, 4]]))
  >>> x.cumsum(axis=0, out=x)
  >>> print(x)
  {
      a: ivy.array([[0],
                   [5]]),
      b: ivy.array([[6, 8, 7],
                   [10, 10, 10]]),
      c: ivy.array([[1, 2],
                   [4, 6],
                   [10, 10]])
  }
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code: def cumsum(
      self: ivy.Container,
      axis: Union[int, ivy.Container] = 0,
      exclusive: Union[bool, ivy.Container] = False,
      reverse: Union[bool, ivy.Container] = False,
      *,
      key_chains: Optional[
          Union[List[str], Dict[str, str], ivy.Container]
      ] = None,
      to_apply: Union[bool, ivy.Container] = True,
      prune_unapplied: Union[bool, ivy.Container] = False,
      map_sequences: Union[bool, ivy.Container] = False,
      dtype: Optional[
          Union[ivy.Dtype, ivy.NativeDtype, ivy.Container]
      ] = None,
      out: Optional[ivy.Container] = None,
  ) -> ivy.Container:
      """ivy.Container instance method variant of ivy.cumsum. This method
      simply wraps the function, and so the docstring for ivy.cumsum also
      applies to this method with minimal changes.
  
      Parameters
      ----------
      self
          Input container to apply cumsum at leaves.
      axis
          Axis along which the cumulative sum is computed. Default is ``0``.
      exclusive
          Whether to perform cumsum exclusively. Default is ``False``.
      reverse
          Whether to perform the cumsum from last to first element in the selected
          axis. Default is ``False`` (from first to last element)
      key_chains
          The key-chains to apply or not apply the method to. Default is ``None``.
      to_apply
          If True, the method will be applied to key_chains, otherwise key_chains
          will be skipped. Default is ``True``.
      prune_unapplied
          Whether to prune key_chains for which the function was not applied.
          Default is ``False``.
      map_sequences
          Whether to also map method to sequences (lists, tuples).
          Default is ``False``.
      dtype
          Data type of the returned array. Default is ``None``.
      out
          Optional output container. Default is ``None``.
  
      Returns
      -------
      ret
          Container whose leaves hold the result of applying cumsum
          at each original leaf arrays along the specified axis.
  
      Examples
      --------
      With :class:`ivy.Container` input:
  
      >>> x = ivy.Container(a=ivy.array([[1, 2, 3],
      ...                                [2, 4, 5]]),
      ...                   b=ivy.array([[4, 5, 6],
      ...                                [2, 3, 1 ]]))
      >>> y = x.cumsum(axis=0, dtype='float64')
      >>> print(y)
      {
          a: ivy.array([[1., 2., 3.],
                        [3., 6., 8.]]),
          b: ivy.array([[4., 5., 6.],
                        [6., 8., 7.]])
      }
  
      >>> x = ivy.Container(a=ivy.array([[1, 3, 4],
      ...                                [5, 7, 8],
      ...                                [9, 10, 11]]),
      ...                   b=ivy.array([[3, 4, 5],
      ...                                [4, 5, 6],
      ...                                [5, 6, 7]]))
      >>> y = ivy.Container(a= ivy.zeros((3, 3)), b= ivy.zeros((3, 3)))
      >>> x.cumsum(axis=1, exclusive=False, reverse=True, out=y)
      >>> print(y)
      {
          a: ivy.array([[8, 7, 4],
                        [20, 15, 8],
                        [30, 21, 11]]),
          b: ivy.array([[12, 9, 5],
                        [15, 11, 6],
                        [18, 13, 7]])
      }
  
      >>> x = ivy.Container(a=ivy.array([[1, 3, 4]]),
      ...                   b=ivy.array([[3, 5, 8],
      ...                                [5, 6, 5]]),
      ...                   c=ivy.array([[2, 4, 1],
      ...                                [3, 6, 9],
      ...                                [0, 2, 3]]))
      >>> y = ivy.Container(a = ivy.zeros((1, 3)),
      ...                   b = ivy.zeros((2, 3)),
      ...                   c = ivy.zeros((3,3)))
      >>> x.cumsum(axis=1,exclusive=True, reverse=False, out=y)
      >>> print(y)
      {
          a: ivy.array([[0, 1, 4]]),
          b: ivy.array([[0, 3, 8],
                        [0, 5, 11]]),
          c: ivy.array([[0, 2, 6],
                        [0, 3, 9],
                        [0, 0, 2]])
      }
  
      >>> x = ivy.Container(a=ivy.array([[0, 3, 2],
      ...                                [5, 10, 2],
      ...                                [1, 10, 1]]),
      ...                   b=ivy.array([[2, 4, 5],
      ...                                [4, 5, 5],
      ...                                [0, 1, 3]]))
      >>> y = x.cumsum(axis=1,exclusive=True, reverse=True, dtype='int64')
      >>> print(y)
      {
          a: ivy.array([[5, 2, 0],
                        [12, 2, 0],
                        [11, 1, 0]]),
          b: ivy.array([[9, 5, 0],
                        [10, 5, 0],
                        [4, 3, 0]])
      }
  
      >>> x = ivy.Container(a=ivy.array([[0],
      ...                                [5]]),
      ...                   b=ivy.array([[6, 8, 7],
      ...                                [4, 2, 3]]),
      ...                   c=ivy.array([[1, 2],
      ...                                [3, 4],
      ...                                [6, 4]]))
      >>> x.cumsum(axis=0, out=x)
      >>> print(x)
      {
          a: ivy.array([[0],
                       [5]]),
          b: ivy.array([[6, 8, 7],
                       [10, 10, 10]]),
          c: ivy.array([[1, 2],
                       [4, 6],
                       [10, 10]])
      }
      """
      return self._static_cumsum(
          self,
          axis=axis,
          exclusive=exclusive,
          reverse=reverse,
          key_chains=key_chains,
          to_apply=to_apply,
          prune_unapplied=prune_unapplied,
          map_sequences=map_sequences,
          dtype=dtype,
          out=out,
      )
  
imports: from typing import Optional, Union, List, Dict, Sequence
  import ivy
  from ivy.data_classes.container.base import ContainerBase
label_desc: Use image node_id 13 for calling the _ContainerWithStatistical obj's underlying member method code with example usage: obj.cumsum(axis, exclusive, reverse) and returns: self