code: def forward(self, x):
      batch = x.size(0)
      if self.radix > 1:
          x = x.view(batch, self.cardinality, self.radix, -1).transpose(
              1, 2
          )
          x = F.softmax(x, dim=1)
          x = x.reshape(batch, -1)
      else:
          x = torch.sigmoid(x)
      return x
  
file_path: pytorch-image-models/timm/layers/split_attn.py
imports: import torch
  import torch.nn.functional
  from torch import nn
  from .helpers import make_divisible
label_desc: Use image node_id 2 for calling the RadixSoftmax obj's underlying member method code with example usage: obj.forward(x) and returns: x