code: def inverse_sigmoid(x, eps=1e-5):
      """Inverse function of sigmoid.
  
      Args:
          x (Tensor): The tensor to do the
              inverse.
          eps (float): EPS avoid numerical
              overflow. Defaults 1e-5.
      Returns:
          Tensor: The x has passed the inverse
              function of sigmoid, has same
              shape with input.
      """
      x = x.clamp(min=0, max=1)
      x1 = x.clamp(min=eps)
      x2 = (1 - x).clamp(min=eps)
      return torch.log(x1 / x2)
  
imports: import math
  import warnings
  from typing import Sequence
  import torch
  import torch.nn
  import torch.nn.functional
  import torch.utils.checkpoint
  from mmcv.cnn import Linear, build_activation_layer, build_norm_layer, xavier_init
  from mmcv.cnn.bricks.drop import build_dropout
  from mmcv.cnn.bricks.registry import FEEDFORWARD_NETWORK, TRANSFORMER_LAYER, TRANSFORMER_LAYER_SEQUENCE
  from mmcv.cnn.bricks.transformer import BaseTransformerLayer, TransformerLayerSequence, build_transformer_layer_sequence
  from mmcv.runner.base_module import BaseModule, Sequential
  from mmcv.utils import deprecated_api_warning, to_2tuple
  from torch.nn.init import normal_
  from ..builder import TRANSFORMER
label_desc: Use image node_id 1 for calling a global function with example usage: inverse_sigmoid(x, eps) and returns: torch