code: def inplace_update(
      x: Union[ivy.Array, paddle.Tensor],
      val: Union[ivy.Array, paddle.Tensor],
      /,
      *,
      ensure_in_backend: bool = False,
      keep_input_dtype: bool = False,
  ) -> ivy.Array:
      _check_inplace_update_support(x, ensure_in_backend)
      if ivy.is_array(x) and ivy.is_array(val):
          (x_native, val_native), _ = ivy.args_to_native(x, val)
  
          if val_native.shape == x_native.shape:
              if x_native.dtype != val_native.dtype:
                  x_native = x_native.astype(val_native.dtype)
              paddle.assign(val_native, x_native)
          else:
              x_native = val_native
          if ivy.is_native_array(x):
              return x_native
          if ivy.is_ivy_array(x):
              x.data = x_native
          else:
              x = ivy.Array(x_native)
          return x
      else:
          return val
  
imports: from numbers import Number
  from typing import Optional, Union, Sequence, Callable, List, Tuple
  import paddle
  import numpy
  import multiprocessing
  import ivy
  import ivy.functional.backends.paddle
  from ivy.func_wrapper import with_unsupported_device_and_dtypes
  from ivy.functional.ivy.general import _broadcast_to
  from ivy.utils.exceptions import _check_inplace_update_support
  from .None import backend_version
label_desc: Use image node_id 17 for calling a global function with example usage: inplace_update() and returns: x, val, x_native