class_docstr: Radix Softmax module in ``SplitAttentionConv2d``.
  
  Args:
      radix (int): Radix of input.
      groups (int): Groups of input.
class_docstr_tok: ["Radix","Softmax","module","in","``","SplitAttentionConv2d","``",".","Args",":","radix","(","int",")",":","Radix","of","input",".","groups","(","int",")",":","Groups","of","input","."]
code: def __init__(self, radix, groups):
      super().__init__()
      self.radix = radix
      self.groups = groups
  
imports: import math
  import torch
  import torch.nn
  import torch.nn.functional
  import torch.utils.checkpoint
  from mmcv.cnn import build_conv_layer, build_norm_layer
  from ..builder import BACKBONES
  from ..utils import ResLayer
  from .resnet import Bottleneck
  from .resnet import ResNetV1d
label_desc: Use image node_id 1 to create a new RSoftmax object from inherited base classes: nn with example: obj = RSoftmax(radix, groups)