code: def forward(ctx, t):
      if t.itype == torch.int32:
          # The dequantization operation requires data to be cast to the scale float type before multiplication
          # by the scale, but this might actually overflow for float16/bfloat16
          return (t._scale.to(torch.float32) * t._data).to(
              t._scale.dtype
          )
      elif t.itype.is_floating_point:
          # Upcast explicitly to the scale dtype
          return t._scale * t._data.to(t._scale.dtype)
      return t._scale * t._data
  
imports: from dataclasses import dataclass
  from typing import Optional
  import torch
  from torch.autograd import Function
  from torch.utils import _pytree
label_desc: Use image node_id 1 for calling the Dequantizer obj's underlying member method code with example usage: obj.forward(ctx, t) and returns: unknown, unknown, unknown