code: def forward(self, x: Any) -> Any:
      if self.hard_clip:
          with torch.no_grad():
              return x.clamp_(min=self.min_val, max=self.max_val)
      else:
          return (
              torch.sigmoid(x) * (self.max_val - self.min_val)
          ) + self.min_val
  
imports: from typing import Any, Optional
  import torch
  from torch import Tensor, nn
label_desc: Use image node_id 2 for calling the Clip obj's underlying member method code with example usage: obj.forward(x) and returns: unknown, x