code: def cumsum(data, axis=None, dtype=None, exclusive=None):
      """Numpy style cumsum op. Return the cumulative inclusive sum of the elements along
      a given axis.
  
      Parameters
      ----------
      data : relay.Expr
          The input data to the operator.
  
      axis : int, optional
          Axis along which the cumulative sum is computed. The default (None) is to compute
          the cumsum over the flattened array.
  
      dtype : string, optional
          Type of the returned array and of the accumulator in which the elements are summed.
          If dtype is not specified, it defaults to the dtype of data.
  
      exclusive : bool, optional
          If true will return exclusive sum in which the first element is not
          included. In other terms, if true, the j-th output element would be
          the sum of the first (j-1) elements. Otherwise, it would be the sum of
          the first j elements.
  
      Returns
      -------
      result : relay.Expr
          The result has the same size as data, and the same shape as data if axis is not None.
          If axis is None, the result is a 1-d array.
  
      Examples
      --------
      .. code-block:: python
  
          a = [[1, 2, 3], [4, 5, 6]]
  
          cumsum(a)  # if axis is not provided, cumsum is done over the flattened input.
          -> [ 1,  3,  6, 10, 15, 21]
  
          cumsum(a, dtype="float32")
          -> [  1.,   3.,   6.,  10.,  15.,  21.]
  
          cumsum(a, axis=0)  # sum over rows for each of the 3 columns
          -> [[1, 2, 3],
              [5, 7, 9]]
  
          cumsum(a, axis=1)
          -> [[ 1,  3,  6],
              [ 4,  9, 15]]
  
          a = [1, 0, 1, 0, 1, 1, 0]  # a is a boolean array
          cumsum(a, dtype=int32)  # dtype should be provided to get the expected results
          -> [1, 1, 2, 2, 3, 4, 4]
      """
      return _make.cumsum(data, axis, dtype, exclusive)
  
imports: from ...tir import expr
  from ..expr import Constant, Expr, Tuple, TupleWrapper, const
  from .None import _make
  from .dyn import _make
  from .tensor import shape_of
label_desc: Use image node_id 44 for calling a global function with example usage: cumsum(data, axis, dtype, exclusive) and returns: _make