code: def inplace_update(
      x: Union[ivy.Array, torch.Tensor],
      val: Union[ivy.Array, torch.Tensor],
      /,
      *,
      ensure_in_backend: bool = False,
      keep_input_dtype: bool = False,
  ) -> ivy.Array:
      ivy.utils.assertions.check_inplace_sizes_valid(x, val)
      if ivy.is_array(x) and ivy.is_array(val):
          if keep_input_dtype:
              val = ivy.astype(val, x.dtype)
          (x_native, val_native), _ = ivy.args_to_native(x, val)
          if is_variable(x_native):
              x_native.copy_ = val_native
          else:
              x_native[()] = val_native
          x_native = x_native.to(val_native.device)
          if ivy.is_native_array(x):
              return x_native
          if ivy.is_ivy_array(x):
              x.data = x_native
              _update_torch_views(x)
          else:
              x = ivy.to_ivy(x_native)
          if ensure_in_backend:
              x._data = val_native
          return x
      else:
          return val
  
imports: from functools import reduce
  from numbers import Number
  from operator import mul
  from typing import Optional, Union, Sequence, Callable, List, Tuple
  import numpy
  import torch
  import ivy
  from ivy.func_wrapper import with_unsupported_dtypes, _update_torch_views
  from .None import backend_version, is_variable
  from ...ivy.general import _broadcast_to
label_desc: Use image node_id 19 for calling a global function with example usage: inplace_update() and returns: x, val, x_native