code: def get_B1_T2_U3_D5_data(dtype=torch.float32, device=CPU_DEVICE):
      logits = torch.tensor(
          [
              0.1,
              0.6,
              0.1,
              0.1,
              0.1,
              0.1,
              0.1,
              0.6,
              0.1,
              0.1,
              0.1,
              0.1,
              0.2,
              0.8,
              0.1,
              0.1,
              0.6,
              0.1,
              0.1,
              0.1,
              0.1,
              0.1,
              0.2,
              0.1,
              0.1,
              0.7,
              0.1,
              0.2,
              0.1,
              0.1,
          ],
          dtype=dtype,
          device=device,
      ).reshape(1, 2, 3, 5)
      logits.requires_grad_(True)
  
      def grad_hook(grad):
          logits.saved_grad = grad.clone()
  
      logits.register_hook(grad_hook)
  
      targets = torch.tensor([[1, 2]], dtype=torch.int32, device=device)
      logit_lengths = torch.tensor(
          [2], dtype=torch.int32, device=device
      )
      target_lengths = torch.tensor(
          [2], dtype=torch.int32, device=device
      )
  
      blank = -1
  
      ref_costs = torch.tensor([5.09566688538], dtype=dtype)
      ref_gradients = torch.tensor(
          [
              0.17703132,
              -0.39992708,
              0.17703132,
              0.17703132,
              -0.13116692,
              0.12247062,
              0.12247062,
              -0.181684,
              0.12247062,
              -0.1857276,
              0.06269141,
              0.06269141,
              0.06928471,
              0.12624498,
              -0.32091248,
              0.05456069,
              -0.2182428,
              0.05456069,
              0.05456069,
              0.05456069,
              0.12073967,
              0.12073967,
              -0.48295838,
              0.12073967,
              0.12073967,
              0.30741188,
              0.16871123,
              0.18645471,
              0.16871123,
              -0.83128875,
          ],
          dtype=dtype,
      ).reshape(1, 2, 3, 5)
  
      data = {
          "logits": logits,
          "targets": targets,
          "logit_lengths": logit_lengths,
          "target_lengths": target_lengths,
          "blank": blank,
      }
  
      return data, ref_costs, ref_gradients
  
imports: import random
  import unittest
  import numpy
  import torch
  from torchaudio.functional import rnnt_loss
label_desc: Use image node_id 5 for calling a global function with example usage: get_B1_T2_U3_D5_data(dtype, device) and returns: data,  ref_costs,  ref_gradients