18b6c1229b68daeeaaef2266d82ca475f83a7445,torchdiffeq/_impl/rk_common.py,RKAdaptiveStepsizeODESolver,__init__,#RKAdaptiveStepsizeODESolver#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#,113

Before Change

        self.max_num_steps = torch.as_tensor(max_num_steps, dtype=torch.int32, device=device)
        grid_points = torch.tensor([], dtype=dtype, device=device) if grid_points is None else grid_points.to(dtype)
        self.grid_points = grid_points
        self.eps = eps if eps is None else torch.as_tensor(eps, dtype=dtype, device=device)
        self.dtype = dtype

        // Copy from class to instance to set device
        self.tableau = _ButcherTableau(alpha=self.tableau.alpha.to(device=device, dtype=y0.dtype),

After Change

        else:
            _check_timelike("step_t", step_t, False)
            step_t = step_t.to(dtype)
        if jump_t is None:
            jump_t = torch.tensor([], dtype=dtype, device=device)
        else:
            _check_timelike("jump_t", jump_t, False)
            jump_t = jump_t.to(dtype)
        counts = torch.cat([step_t, jump_t]).unique(return_counts=True)[1]
        if (counts > 1).any():
            raise ValueError("`step_t` and `jump_t` must not have any repeated elements between them.")

Instances