code: def inplace_update(
      x: Union[ivy.Array, tf.Tensor],
      val: Union[ivy.Array, tf.Tensor],
      /,
      *,
      ensure_in_backend: bool = False,
      keep_input_dtype: bool = False,
  ) -> ivy.Array:
      if ivy.is_array(x) and ivy.is_array(val):
          _check_inplace_update_support(x, ensure_in_backend)
          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.assign(val_native)
              if ivy.is_ivy_array(x):
                  x.data = x_native
              else:
                  x = ivy.Array(x_native)
          elif ivy.is_ivy_array(x):
              x.data = val_native
              # Handle view updates
              if ivy.exists(x._base):
                  base = x._base
                  base_idx = ivy.arange(base.size).reshape(base.shape)
                  for fn, args, kwargs, index in x._manipulation_stack:
                      kwargs["copy"] = True
                      base_idx = ivy.__dict__[fn](
                          base_idx, *args, **kwargs
                      )
                      base_idx = (
                          base_idx[index]
                          if ivy.exists(index)
                          else base_idx
                      )
                  base_flat = tf.reshape(base.data, -1)
                  base_flat = tf.tensor_scatter_nd_update(
                      base_flat,
                      tf.reshape(base_idx.data, (-1, 1)),
                      tf.reshape(val_native, -1),
                  )
  
                  base.data = tf.reshape(base_flat, base.shape)
                  for ref in base._view_refs:
                      view = ref()
                      if ivy.exists(view) and view is not x:
                          _update_view(view, base)
              else:
                  for ref in x._view_refs:
                      view = ref()
                      if ivy.exists(view):
                          _update_view(view, x)
          else:
              x = ivy.to_ivy(x_native)
          return x
      else:
          return val
  
imports: from typing import Optional, Union, Sequence, Callable, Tuple
  import numpy
  import multiprocessing
  from numbers import Number
  import tensorflow
  import ivy
  from ivy.functional.ivy.gradients import _is_variable
  from ivy.func_wrapper import with_unsupported_dtypes
  from ivy.utils.exceptions import _check_inplace_update_support
  from .None import backend_version
  from ...ivy.general import _broadcast_to
label_desc: Use image node_id 17 for calling a global function with example usage: inplace_update() and returns: x, val