code: def logit(
      self: TensorLikeType, eps: Optional[float] = None
  ) -> TensorLikeType:
      if eps is None:
          eps = -1.0
      lo = eps
      hi = 1 - eps
      self = torch.clamp(self, lo, hi)
      return torch.log(torch.true_divide(self, torch.sub(1, self)))
  
imports: import math
  from typing import Optional, Union
  import torch
  import torch._prims
  import torch._prims_common
  import torch._refs
  from torch import Tensor
  from torch._decomp import register_decomposition
  from torch._prims_common import ELEMENTWISE_TYPE_PROMOTION_KIND, Number, NumberType, TensorLike, TensorLikeType
  from torch._prims_common.wrappers import elementwise_type_promotion_wrapper, out_wrapper
  from torch._refs import _make_alias, _make_elementwise_binary_reference, _make_elementwise_unary_reference
label_desc: Use image node_id 9 for calling a global function with example usage: logit(eps) and returns: torch