code: def logit(
      x: paddle.Tensor,
      /,
      *,
      eps: Optional[float] = None,
      complex_mode: Literal["split", "magnitude", "jax"] = "jax",
      out=None,
  ):
      if x.dtype in [paddle.float32, paddle.float64]:
          return paddle.logit(x, eps)
      if eps is None:
          nan = paddle_backend.squeeze(
              paddle.to_tensor(float("nan"), dtype=x.dtype), axis=-1
          )
          x = paddle_backend.where(
              paddle_backend.logical_or(
                  paddle_backend.greater(x, 1),
                  paddle_backend.less(x, 0),
              ),
              nan,
              x,
          )
      else:
          x = paddle_backend.minimum(
              paddle_backend.maximum(x, eps), 1 - eps
          )
      return paddle_backend.log(
          paddle_backend.divide(x, paddle_backend.subtract(1, x))
      ).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 1 for calling a global function with example usage: logit() and returns: paddle_backend, paddle