code: def cumprod(
      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)
          else:
              dtype = _infer_dtype(x.dtype)
      if not (exclusive or reverse):
          return jnp.cumprod(x, axis, dtype=dtype)
      elif exclusive and reverse:
          x = jnp.cumprod(
              jnp.flip(x, axis=(axis,)), axis=axis, dtype=dtype
          )
          x = jnp.swapaxes(x, axis, -1)
          x = jnp.concatenate(
              (jnp.ones_like(x[..., -1:]), x[..., :-1]), -1
          )
          x = jnp.swapaxes(x, axis, -1)
          return jnp.flip(x, axis=(axis,))
  
      elif exclusive:
          x = jnp.swapaxes(x, axis, -1)
          x = jnp.concatenate(
              (jnp.ones_like(x[..., -1:]), x[..., :-1]), -1
          )
          x = jnp.cumprod(x, -1, dtype=dtype)
          return jnp.swapaxes(x, axis, -1)
      else:
          x = jnp.cumprod(
              jnp.flip(x, axis=(axis,)), axis=axis, dtype=dtype
          )
          return jnp.flip(x, axis=axis)
  
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 9 for calling a global function with example usage: cumprod() and returns: jnp, jnp, jnp, jnp