code: def y_exact(self, t):
      t_numpy = t.detach().cpu().numpy()
      A_np = self.A.detach().cpu().numpy()
      ans = []
      for t_i in t_numpy:
          ans.append(
              np.matmul(scipy.linalg.expm(A_np * t_i), self.initial_val)
          )
      return (
          torch.stack([torch.tensor(ans_) for ans_ in ans])
          .reshape(len(t_numpy), self.dim)
          .to(t)
      )
  
file_path: torchdiffeq/tests/problems.py
imports: import math
  import numpy
  import scipy.linalg
  import torch
label_desc: Use image node_id 3 for calling the LinearODE obj's underlying member method code with example usage: obj.y_exact(t) and returns: torch