def set_options(logsig, return_sequences=False, eps=1e-5):
    """Sets the options to be passed to the relevant `odeint` function.
    Args:
        logsig (torch.Tensor): The logsignature of the path.
        return_sequences (bool): Set True if a regression problem where we need the full sequence. This requires us
            specifying the time grid as `torch.arange(0, T_final)` which is less memory efficient that specifying
            the times `t = torch.Tensor([0, T_final])` along with an `step_size=1` in the options.
        eps (float): The epsilon perturbation to make to integration points to distinguish the ends.
    Returns:
        torch.Tensor, dict: The integration times and the options dictionary.
    """
    length = logsig.size(1) + 1
    if return_sequences:
        t = torch.arange(0, length, dtype=torch.float).to(logsig.device)
        options = {'eps': eps}
    else:
        options = {'step_size': 1, 'eps': eps}
        t = torch.Tensor([0, length]).to(logsig.device)
    return t, options
Sets the options to be passed to the relevant `odeint` function.
    Args:
        logsig (torch.Tensor): The logsignature of the path.
        return_sequences (bool): Set True if a regression problem where we need the full sequence. This requires us
            specifying the time grid as `torch.arange(0, T_final)` which is less memory efficient that specifying
            the times `t = torch.Tensor([0, T_final])` along with an `step_size=1` in the options.
        eps (float): The epsilon perturbation to make to integration points to distinguish the ends.
    Returns:
        torch.Tensor, dict: The integration times and the options dictionary.
    
analyze/convnexts4nd/src/models/baselines/nrde.py