code: def clip(
      x: paddle.Tensor,
      /,
      x_min: Optional[Union[Number, paddle.Tensor]] = None,
      x_max: Optional[Union[Number, paddle.Tensor]] = None,
      *,
      out: Optional[paddle.Tensor] = None,
  ) -> paddle.Tensor:
      if x_min is None and x_max is None:
          raise ValueError(
              "At least one of the x_min or x_max must be provided"
          )
      promoted_type = x.dtype
      if x_min is not None:
          if not hasattr(x_min, "dtype"):
              x_min = ivy.array(x_min).data
          promoted_type = ivy.as_native_dtype(
              ivy.promote_types(x.dtype, x_min.dtype)
          )
          x = paddle_backend.maximum(
              paddle.cast(x, promoted_type),
              paddle.cast(x_min, promoted_type),
          )
      if x_max is not None:
          if not hasattr(x_max, "dtype"):
              x_max = ivy.array(x_max).data
          promoted_type = ivy.as_native_dtype(
              ivy.promote_types(promoted_type, x_max.dtype)
          )
          x = paddle_backend.minimum(
              paddle.cast(x, promoted_type),
              paddle.cast(x_max, promoted_type),
          )
      return x
  
imports: from numbers import Number
  from typing import Union, Optional, Tuple, List, Sequence, Iterable
  import math
  import paddle
  import ivy
  import ivy.functional.backends.paddle
  from ivy.func_wrapper import with_unsupported_device_and_dtypes, with_unsupported_dtypes, with_supported_dtypes
  from .None import backend_version
  from ...ivy.manipulation import _calculate_out_shape
label_desc: Use image node_id 16 for calling a global function with example usage: clip(x_min, x_max) and returns: x