code: def silu(
      x: paddle.Tensor, /, *, out: Optional[paddle.Tensor] = None
  ) -> paddle.Tensor:
      if paddle.is_complex(x):
          return x * (1.0 / (1.0 + paddle_backend.exp(-x)))
      return F.silu(x)
  
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 6 for calling a global function with example usage: silu() and returns: F, unknown