class_docstr: ### Group normalization with float32 casting
code: def forward(self, x):
      return super().forward(x.float()).type(x.dtype)
  
imports: import math
  from typing import List
  import numpy
  import torch
  import torch.nn
  import torch.nn.functional
  from labml_nn.diffusion.stable_diffusion.model.unet_attention import SpatialTransformer
label_desc: Use image node_id 1 for calling the GroupNorm32 obj's underlying member method code with example usage: obj.forward(x) and returns: super