code: def logsigmoid(
      input: paddle.Tensor,
      /,
      *,
      complex_mode="jax",
      out: Optional[paddle.Tensor] = None,
  ) -> paddle.Tensor:
      if paddle.is_complex(input):
          return paddle_backend.log(
              paddle_backend.divide(
                  1.0,
                  (paddle_backend.add(1.0, paddle_backend.exp(-input))),
              )
          )
      return F.log_sigmoid(input)
  
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 4 for calling a global function with example usage: logsigmoid() and returns: F, paddle_backend