class_docstr: Applies Group Normalization in single-precision.
class_docstr_tok: ["Applies","Group","Normalization","in","single-precision","."]
code: def forward(self, x: Tensor) -> Tensor:
      w, b = self.weight, self.bias
  
      fp32_x = x.float()
      fp32_w = w.float()
      fp32_b = b.float() if b is not None else None
  
      y = group_norm(fp32_x, self.num_groups, fp32_w, fp32_b, self.eps)
  
      return y.type_as(x)
  
imports: from typing import List, Optional, Sequence, Tuple, final
  import torch
  import torch.nn
  from torch import Tensor
  from torch.nn import GELU, Conv1d, Dropout, GroupNorm, Module, Sequential
  from torch.nn.functional import group_norm, layer_norm
  from fairseq2.models.feature_extractor import SequenceFeatureExtractor
  from fairseq2.nn.normalization import LayerNorm
  from fairseq2.nn.padding import PaddingMask
  from fairseq2.nn.utils.grad import scale_grad
  from fairseq2.typing import DataType, Device, finaloverride, override
label_desc: Use image node_id 1 for calling the Float32GroupNorm obj's underlying member method code with example usage: obj.forward(x) and returns: y