def _check_first_cat(first_cat: Operator, second_cat: Operator) -> bool:
    """
    return True if the first cat is valid for fusion
    """
    # Make sure input_accessors do not carry any strided information.
    # It may happen. For example, an input of the cat can be of a strided
    # tensor generated by slice, which takes another concat's output.
    # Something like below:
    #     y1 = concat(x0, x1)
    #     y2 = slice(y1)
    #     y = cat(y1, y2)
    # In such a case, we cannot merge those two concat ops.
    if not all(
        accessor.actual_shapes is None
        for accessor in first_cat._attrs["input_accessors"]
    ):
        return False
    if not all(first_cat._attrs["input_masks"]):
        return False

    # we need to make sure all other dst ops except the second cat have input
    # accessors for which we may generate valid strided information. We will
    # leverage the input accessor by injecting a slice op between the merged
    # cat and the strided op (e.g. add).
    cat_dim = first_cat._attrs["concat_dim"]
    first_cat_outputs = first_cat._attrs["outputs"]
    assert (
        len(first_cat_outputs) == 1
    ), f"expected {first_cat_outputs=} to have a single output"
    first_cat_output = first_cat_outputs[0]
    first_cat_dst_ops = first_cat_output._attrs["dst_ops"]
    if len(first_cat_dst_ops) == 1:
        return True
    if not transform_strided_ops_utils.cat_split_dim_is_static(first_cat, cat_dim):
        return False
    # we cannot leverage slice if any of the dimensions after cat_dim is dynamic
    if not shape_utils.all_static_dimensions(first_cat_output.shape(), cat_dim):
        return False

    # we can fuse the first cat into the second only if all of the first cat's
    # dst ops are valid
    for dst_op in first_cat_dst_ops:
        if dst_op is second_cat:
            continue
        if not _is_supported_dst_op_for_first_cat(dst_op):
            return False
        # merging first_cat and second_cat may introduce a cycle
        if transform_utils.is_ancestor(dst_op, second_cat):
            return False
    return True
return True if the first cat is valid for fusion
python/aitemplate/compiler/transform/transform_memory_ops.py