code: def torch_group_norm(x, g, w, b, eps, act=""):
      xdtype, wdtype = x.dtype, w.dtype
      if xdtype != wdtype:
          x = x.to(dtype=wdtype)
      y = torch.nn.functional.group_norm(x, g, w, b, eps)
      if act in ["silu", "swish"]:
          y = torch.nn.functional.silu(y)
      if xdtype != wdtype and y.dtype != xdtype:
          y = y.to(dtype=xdtype)
      return y
  
imports: import torch
  import torch.nn.functional
  import torch.nn.init
  import group_norm_cuda
  from torch import Tensor
  from torch.nn.parameter import Parameter
  from torch._dynamo import disable
  from functools import partial
label_desc: Use image node_id 1 for calling a global function with example usage: torch_group_norm(x, g, w, b, eps, act) and returns: y