def METHOD_NAME(model: Model, proximal_weights: Tensor) -> None:
    r"""Validate model
    Perform vaidation checks on model used in base acquisition function to make sure
    it is compatible with proximal weighting.
    Args:
        model: Model associated with base acquisition function to be validated.
        proximal_weights: A `d` dim tensor used to bias locality
                along each axis.
    """
    # check model for train_inputs and single batch
    if not hasattr(model, "train_inputs"):
        raise UnsupportedError("Acquisition function model must have `train_inputs`.")
    # get train inputs for each type of possible model
    if isinstance(model, ModelListGP):
        # ModelListGP models
        # check to make sure that the training inputs and input transformers for each
        # model match and are reversible
        train_inputs = model.train_inputs[0][0]
        input_transform = _get_input_transform(model.models[0])
        for i in range(len(model.train_inputs)):
            if not torch.equal(train_inputs, model.train_inputs[i][0]):
                raise UnsupportedError(
                    "Proximal acquisition function does not support unequal "
                    "training inputs"
                )
            if not _compare_input_transforms(
                input_transform, _get_input_transform(model.models[i])
            ):
                raise UnsupportedError(
                    "Proximal acquisition function does not support non-identical "
                    "input transforms"
                )
    else:
        # any non-ModelListGP model
        train_inputs = model.train_inputs[0]
    # check to make sure that the model is single t-batch (q-batches are allowed)
    if model.batch_shape != torch.Size([]) and train_inputs.shape[1] != 1:
        raise UnsupportedError(
            "Proximal acquisition function requires a single batch model"
        )
    # check to make sure that weights match the training data shape
    if (
        len(proximal_weights.shape) != 1
        or proximal_weights.shape[0] != train_inputs.shape[-1]
    ):
        raise ValueError(
            "`proximal_weights` must be a one dimensional tensor with "
            "same feature dimension as model."
        )