def _update_cat_dst_ops(
    first_cat: Operator, second_cat: Operator, cat_dim_offset: int
) -> None:
    """
    Add all the strided dst_ops of the first cat to the second and
    make an appropriate slice op between the second cat and each dst_ops.
    cat_dim_offset represents the offset of the first cat output appearing
    in the second cat along the cat_dim dimension.
    """
    first_cat_output = first_cat._attrs["outputs"][0]
    first_cat_dst_ops = first_cat_output._attrs["dst_ops"]
    # the first cat does not have any strided ops
    if len(first_cat_dst_ops) <= 1:
        return
    first_cat_shape = first_cat_output.shape()
    rank = len(first_cat_shape)
    cat_dim = first_cat._attrs["concat_dim"]
    assert transform_strided_ops_utils.cat_split_dim_is_static(
        first_cat, cat_dim
    ), f"expected the {cat_dim=} of {first_cat=} to be static"
    second_cat_output = second_cat._attrs["outputs"][0]
    # make start_indices and end_indices for the slice
    for idx, first_cat_dst_op in enumerate(first_cat_dst_ops):
        if first_cat_dst_op is second_cat:
            continue
        else:
            # Make a new slice op. Note that it's fine we make a new slice op from
            # another slice op, because consecutive slice ops will be merged
            # by the merge_slice_ops pass
            slice_start_indices = [0] * rank
            slice_end_indices = [None] * rank
            slice_start_indices[cat_dim] = cat_dim_offset
            slice_end_indices[cat_dim] = (
                cat_dim_offset + first_cat_shape[cat_dim].value()
            )
            slice_op = dynamic_slice()
            slice_op_name = f'dynamic_slice_{idx}_{first_cat._attrs["name"]}'
            slice_op._attrs["name"] = slice_op_name
            slice_op._attrs["original_name"] = slice_op_name
            slice_output = slice_op(
                second_cat_output, slice_start_indices, slice_end_indices
            )
            slice_output._attrs["name"] = f"{slice_op_name}_0"
            slice_output._attrs["dst_ops"].add(first_cat_dst_op)
            # remove the old strided op from first cat's dst_ops
            first_cat_dst_ops.remove(first_cat_dst_op)
            # update the strided op's input to the newly-created slice output
            first_cat_dst_op.replace_input_tensor(first_cat_output, slice_output)

    Add all the strided dst_ops of the first cat to the second and
    make an appropriate slice op between the second cat and each dst_ops.
    cat_dim_offset represents the offset of the first cat output appearing
    in the second cat along the cat_dim dimension.
    
python/aitemplate/compiler/transform/transform_memory_ops.py