code: def threshold(
      x: paddle.Tensor,
      /,
      *,
      threshold: float,
      value: float,
      out: Optional[paddle.Tensor] = None,
  ) -> paddle.Tensor:
      if x.dtype in [paddle.float32, paddle.float64]:
          return paddle_backend.where(
              paddle_backend.greater(x, threshold), x, value
          )
      if paddle.is_complex(x):
          return paddle_backend.where(
              paddle_backend.greater(x, threshold), x, value
          )
      x = x.cast("float32")
      return paddle_backend.where(
          paddle_backend.greater(x, threshold), x, value
      ).cast(x.dtype)
  
file_path: ivy/ivy/functional/backends/paddle/experimental/activations.py
imports: from typing import Optional, Union, Literal
  import paddle
  import paddle.nn.functional
  import ivy.functional.backends.paddle
  from ivy.func_wrapper import with_unsupported_device_and_dtypes, with_supported_dtypes, with_supported_device_and_dtypes
  from .None import backend_version
label_desc: Use image node_id 10 for calling a global function with example usage: threshold() and returns: paddle_backend, paddle_backend, paddle_backend