code: def cumsum(
      x: JaxArray,
      axis: int = 0,
      exclusive: bool = False,
      reverse: bool = False,
      *,
      dtype: Optional[jnp.dtype] = None,
      out: Optional[JaxArray] = None,
  ) -> JaxArray:
      dtype = ivy.as_native_dtype(dtype)
      if dtype is None:
          if dtype is jnp.bool_:
              dtype = ivy.default_int_dtype(as_native=True)
          elif ivy.is_int_dtype(x.dtype):
              dtype = ivy.promote_types(
                  x.dtype, ivy.default_int_dtype(as_native=True)
              )
          else:
              dtype = _infer_dtype(x.dtype)
      if exclusive or reverse:
          if exclusive and reverse:
              x = jnp.cumsum(
                  jnp.flip(x, axis=axis), axis=axis, dtype=dtype
              )
              x = jnp.swapaxes(x, axis, -1)
              x = jnp.concatenate(
                  (jnp.zeros_like(x[..., -1:]), x[..., :-1]), -1
              )
              x = jnp.swapaxes(x, axis, -1)
              res = jnp.flip(x, axis=axis)
          elif exclusive:
              x = jnp.swapaxes(x, axis, -1)
              x = jnp.concatenate(
                  (jnp.zeros_like(x[..., -1:]), x[..., :-1]), -1
              )
              x = jnp.cumsum(x, -1, dtype=dtype)
              res = jnp.swapaxes(x, axis, -1)
          elif reverse:
              x = jnp.cumsum(
                  jnp.flip(x, axis=axis), axis=axis, dtype=dtype
              )
              res = jnp.flip(x, axis=axis)
          return res
      return jnp.cumsum(x, axis, dtype=dtype)
  
file_path: ivy/ivy/functional/backends/jax/statistical.py
imports: import jax.numpy
  from typing import Union, Optional, Sequence
  import ivy
  from ivy.func_wrapper import with_unsupported_dtypes
  from ivy.functional.backends.jax import JaxArray
  from .None import backend_version
label_desc: Use image node_id 10 for calling a global function with example usage: cumsum(x, axis, exclusive, reverse) and returns: jnp, res