code: def scatter_flat(
      indices: paddle.Tensor,
      updates: paddle.Tensor,
      /,
      *,
      size: Optional[int] = None,
      reduction: str = "sum",
      out: Optional[paddle.Tensor] = None,
  ):
      if indices.dtype not in [paddle.int32, paddle.int64]:
          indices = indices.cast("int64")
      if ivy.exists(size) and ivy.exists(out):
          ivy.utils.assertions.check_equal(out.ndim, 1, as_array=False)
          ivy.utils.assertions.check_equal(
              out.shape[0], size, as_array=False
          )
      return paddle_backend.scatter_nd(
          indices.unsqueeze(-1),
          updates,
          shape=[size],
          reduction=reduction,
          out=out,
      )
  
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 20 for calling a global function with example usage: scatter_flat() and returns: paddle_backend