old_contents: import abc
  import torch
  from scipy.integrate import solve_ivp
  from .misc import _handle_unused_kwargs
  
  
  class ScipyWrapperODESolver(metaclass=abc.ABCMeta):
  
      def __init__(self, func, y0, rtol, atol, min_step=0, max_step=float('inf'), solver="LSODA", **unused_kwargs):
          unused_kwargs.pop('norm', None)
          unused_kwargs.pop('grid_points', None)
          unused_kwargs.pop('eps', None)
          _handle_unused_kwargs(self, unused_kwargs)
          del unused_kwargs
  
          self.dtype = y0.dtype
          self.device = y0.device
          self.shape = y0.shape
          self.y0 = y0.detach().cpu().numpy().reshape(-1)
          self.rtol = rtol
          self.atol = atol
          self.min_step = min_step
          self.max_step = max_step
          self.solver = solver
          self.func = convert_func_to_numpy(func, self.shape, self.device, self.dtype)
  
      def integrate(self, t):
          if t.numel() == 1:
              return torch.tensor(self.y0)[None].to(self.device, self.dtype)
          t = t.detach().cpu().numpy()
          sol = solve_ivp(
              self.func,
              t_span=[t.min(), t.max()],
              y0=self.y0,
              t_eval=t,
              method=self.solver,
              rtol=self.rtol,
              atol=self.atol,
              min_step=self.min_step,
              max_step=self.max_step
          )
          sol = torch.tensor(sol.y).T.to(self.device, self.dtype)
          sol = sol.reshape(-1, *self.shape)
          return sol
  
  
  def convert_func_to_numpy(func, shape, device, dtype):
  
      def np_func(t, y):
          t = torch.tensor(t).to(device, dtype)
          y = torch.reshape(torch.tensor(y).to(device, dtype), shape)
          with torch.no_grad():
              f = func(t, y)
          return f.detach().cpu().numpy().reshape(-1)
  
      return np_func
  
new_contents: import abc
  import torch
  from scipy.integrate import solve_ivp
  from .misc import _handle_unused_kwargs
  
  
  class ScipyWrapperODESolver(metaclass=abc.ABCMeta):
  
      def __init__(self, func, y0, rtol, atol, min_step=0, max_step=float('inf'), solver="LSODA", **unused_kwargs):
          unused_kwargs.pop('norm', None)
          unused_kwargs.pop('grid_points', None)
          unused_kwargs.pop('eps', None)
          _handle_unused_kwargs(self, unused_kwargs)
          del unused_kwargs
  
          self.dtype = y0.dtype
          self.device = y0.device
          self.shape = y0.shape
          self.y0 = y0.detach().cpu().numpy().reshape(-1)
          self.rtol = rtol
          self.atol = atol
          self.min_step = min_step
          self.max_step = max_step
          self.solver = solver
          self.func = convert_func_to_numpy(func, self.shape, self.device, self.dtype)
  
      def integrate(self, t):
          if t.numel() == 1:
              return torch.tensor(self.y0)[None].to(self.device, self.dtype)
          t = t.detach().cpu().numpy()
          sol = solve_ivp(
              self.func,
              t_span=[t.min(), t.max()],
              y0=self.y0,
              t_eval=t,
              method=self.solver,
              rtol=self.rtol,
              atol=self.atol,
              min_step=self.min_step,
              max_step=self.max_step
          )
          sol = torch.tensor(sol.y).T.to(self.device, self.dtype)
          sol = sol.reshape(-1, *self.shape)
          return sol
          
      @classmethod
      def valid_callbacks(cls):
          return set()
  
  
  def convert_func_to_numpy(func, shape, device, dtype):
  
      def np_func(t, y):
          t = torch.tensor(t).to(device, dtype)
          y = torch.reshape(torch.tensor(y).to(device, dtype), shape)
          with torch.no_grad():
              f = func(t, y)
          return f.detach().cpu().numpy().reshape(-1)
  
      return np_func
  
subject: Add valid_callbacks to Scipy wrapper