code: def tanhshrink(
      x: paddle.Tensor, /, *, out: Optional[paddle.Tensor] = None
  ) -> paddle.Tensor:
      if x.dtype in [paddle.float32, paddle.float64]:
          return F.tanhshrink(x)
      if paddle.is_complex(x):
          return paddle.complex(
              F.tanhshrink(x.real()), F.tanhshrink(x.imag())
          )
      return F.tanhshrink(x.cast("float32")).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 9 for calling a global function with example usage: tanhshrink() and returns: F, F, paddle