code: def forward(ctx, x, max_int, vmin, vmax):
      # here assuming 0 =< x =< 1
      x = (x - vmin) / (vmax - vmin)
      x = torch.round(x * max_int) / max_int
      return x * (vmax - vmin) + vmin
  
file_path: advertorch/advertorch/functional.py
imports: import torch
  from torchvision import transforms
  from PIL import Image
label_desc: Use image node_id 1 for calling the FloatToIntSqueezing obj's underlying member method code with example usage: obj.forward(ctx, x, max_int, vmin, vmax) and returns: unknown