code: def logodd_mean(x: Tensor, dim: int, eps: float = 1e-6) -> Tensor:
      """
      Compute log-odd mean along given dimension.
      logodd = log(p / (1 - p))
  
      This implementation assume values are in range [0, 1] (Probabilities)
      Args:
          x: Input tensor of arbitrary shape
          dim: Dimension to reduce
  
      Returns:
          Tensor
      """
      x = x.clamp(min=eps, max=1.0 - eps)
      x = torch.log(x / (1 - x))
      x = torch.mean(x, dim=dim)
      x = torch.exp(x) / (1 + torch.exp(x))
      return x
  
imports: import itertools
  from collections.abc import Sized, Iterable
  from typing import Union, Tuple
  import torch
  from torch import Tensor
  from ..utils.support import pytorch_toolbelt_deprecated
label_desc: Use image node_id 25 for calling a global function with example usage: logodd_mean(x, dim, eps) and returns: x