code: def construct_problem(
      device,
      npts=10,
      ode="constant",
      reverse=False,
      dtype=torch.float64,
  ):
      f = PROBLEMS[ode]().to(dtype=dtype, device=device)
  
      t_points = torch.linspace(
          1,
          8,
          npts,
          dtype=torch.float64,
          device=device,
          requires_grad=True,
      )
      sol = f.y_exact(t_points).to(dtype)
  
      def _flip(x, dim):
          indices = [slice(None)] * x.dim()
          indices[dim] = torch.arange(
              x.size(dim) - 1, -1, -1, dtype=torch.long, device=device
          )
          return x[tuple(indices)]
  
      if reverse:
          t_points = _flip(t_points, 0).clone().detach()
          sol = _flip(sol, 0).clone().detach()
  
      return f, sol[0].detach().requires_grad_(True), t_points, sol
  
file_path: torchdiffeq/tests/problems.py
imports: import math
  import numpy
  import scipy.linalg
  import torch
label_desc: Use image node_id 1 for calling a global function with example usage: construct_problem(device, npts, ode, reverse, dtype) and returns: f,  sol,  t_points,  sol, x