code: def gen_input(self, for_vector_kernel):
      self.B = 4
      T_min = 51
      T_max = 101
      U_min = 12
      U_max = 25
      if for_vector_kernel:
          H = 512
      else:
          H = 509
      dtype = torch.float16
      device = "cuda"
  
      self.f_tst = torch.randn(
          (self.B, T_max, H),
          dtype=dtype,
          requires_grad=True,
          device=device,
      )
      self.g_tst = torch.randn(
          (self.B, U_max, H),
          dtype=dtype,
          requires_grad=True,
          device=device,
      )
      self.h_grad = torch.randn(
          self.B, T_max, U_max, H, dtype=dtype, device=device
      )
      self.f_len = torch.randint(
          T_min, T_max + 1, (self.B,), dtype=torch.int, device=device
      )
      self.g_len = torch.randint(
          U_min, U_max + 1, (self.B,), dtype=torch.int, device=device
      )
      self.f_len[torch.randint(0, self.B, (1,)).item()] = T_max
      self.g_len[torch.randint(0, self.B, (1,)).item()] = U_max
      self.dropout_prob = 0.5
  
      # Make sure gradients from out-of-bound locations are zero. This should be guaranteed by
      # the loss function
      for b in range(self.B):
          self.h_grad[b, self.f_len[b] :, :, :] = 0
          self.h_grad[b, :, self.g_len[b] :, :] = 0
      self.h_grad_packed = self._pack(
          self.h_grad, self.f_len, self.g_len
      )
  
imports: import unittest
  import torch
label_desc: Use image node_id 2 for calling the TransducerJointTest obj's underlying member method code with example usage: obj.gen_input(for_vector_kernel) without return types