code: def test_backward(self, allmasked: bool = False):
      import generic_scaled_masked_softmax_cuda
  
      for qlen, klen in self.q_k_lens:
          inputs = torch.normal(
              0,
              2,
              (self.batch, self.attn, qlen, klen),
              dtype=self.dtype,
              device=self.device,
          )
          backward = torch.rand_like(
              inputs, dtype=torch.float16, device=self.device
          )
          masks = (
              torch.randint(
                  0,
                  2,
                  (self.batch, 1, qlen, klen),
                  dtype=torch.bool,
                  device=self.device,
              )
              if not allmasked
              else torch.ones(
                  (self.batch, 1, qlen, klen),
                  dtype=torch.bool,
                  device=self.device,
              )
          )
          softmax_results = generic_scaled_masked_softmax_cuda.forward(
              inputs, masks, self.scale_t
          )
          back_grad = generic_scaled_masked_softmax_cuda.backward(
              backward, softmax_results, self.scale_t
          )
          inputs.requires_grad = True
          softmax_results_torch = forward_torch_softmax(
              inputs, masks, self.scale_t
          )
          softmax_results_torch.backward(backward)
          self.assertEqual(
              back_grad,
              inputs.grad,
              **self.thresh,
              msg=f"(q, k) = ({qlen, klen})",
          )
  
imports: import itertools
  import torch
  from torch.testing._internal import common_utils
  from apex.transformer import AttnMaskType
  from apex.transformer.functional import FusedScaleMaskSoftmax
label_desc: Use image node_id 4 for calling the TestGenericFusedSoftmaxKernel obj's underlying member method code with example usage: obj.test_backward(allmasked) without return types