def METHOD_NAME(self, num_acquisitions=1, **opt_params):
    """Selects `num_acquisitions` query points at which to query the
    original function by Thompson sampling.
    :param int num_acquisitions: the number of points to generate
    :param dict opt_params: additional parameters for optimization
        routines
    :return: a tensor of points to evaluate `loss` at
    :rtype: torch.Tensor
    """
    # Initialize the return tensor
    X = self.gpmodel.X
    X = torch.empty(num_acquisitions, *X.shape[1:], dtype=X.dtype, device=X.device)
    for i in range(num_acquisitions):
        sampler = self.gpmodel.iter_sample(noiseless=False)
        x, _ = self.opt_differentiable(sampler, **opt_params)
        X[i, ...] = x
    return X