code: def scatter_update(
      ref, indices, updates, use_locking=True, name=None
  ):
      # pylint: disable=line-too-long
      r"""Applies sparse updates to a variable reference.
  
      This operation computes
  
      ```python
          # Scalar indices
          ref[indices, ...] = updates[...]
  
          # Vector indices (for each i)
          ref[indices[i], ...] = updates[i, ...]
  
          # High rank indices (for each i, ..., j)
          ref[indices[i, ..., j], ...] = updates[i, ..., j, ...]
      ```
  
      This operation outputs `ref` after the update is done.
      This makes it easier to chain operations that need to use the reset value.
  
      If values in `ref` is to be updated more than once, because there are
      duplicate entries in `indices`, the order at which the updates happen
      for each value is undefined.
  
      Requires `updates.shape = indices.shape + ref.shape[1:]`.
  
      <div style="width:70%; margin:auto; margin-bottom:10px; margin-top:20px;">
      <img style="width:100%" src="https://www.tensorflow.org/images/ScatterUpdate.png" alt>
      </div>
  
      Args:
        ref: A `Variable`.
        indices: A `Tensor`. Must be one of the following types: `int32`, `int64`.
          A tensor of indices into the first dimension of `ref`.
        updates: A `Tensor`. Must have the same type as `ref`.
          A tensor of updated values to store in `ref`.
        use_locking: An optional `bool`. Defaults to `True`.
          If True, the assignment will be protected by a lock;
          otherwise the behavior is undefined, but may exhibit less contention.
        name: A name for the operation (optional).
  
      Returns:
        Same as `ref`.  Returned as a convenience for operations that want
        to use the updated values after the update is done.
      """
      if ref.dtype._is_ref_dtype:
          return gen_state_ops.scatter_update(
              ref, indices, updates, use_locking=use_locking, name=name
          )
      return ref._lazy_read(
          gen_resource_variable_ops.resource_scatter_update(  # pylint: disable=protected-access
              ref.handle,
              indices,
              ops.convert_to_tensor(updates, ref.dtype),
              name=name,
          )
      )
  
imports: from tensorflow.python.framework import ops
  from tensorflow.python.framework import tensor_shape
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import gen_math_ops
  from tensorflow.python.ops import gen_resource_variable_ops
  from tensorflow.python.ops import gen_state_ops
  from tensorflow.python.ops import state_grad
  from tensorflow.python.ops.gen_state_ops import *
  from tensorflow.python.util import deprecation
  from tensorflow.python.util.deprecation import deprecated
  from tensorflow.python.util.tf_export import tf_export
label_desc: Use image node_id 9 for calling a global function with example usage: scatter_update(ref, indices, updates, use_locking, name) and returns: ref, gen_state_ops