def build(
    inputs: Union[schedule.Schedule, PrimFunc, IRModule, Mapping[str, IRModule]],
    args: Optional[List[Union[Buffer, tensor.Tensor, Var]]] = None,
    target: Optional[Union[str, Target]] = None,
    target_host: Optional[Union[str, Target]] = None,
    name: Optional[str] = "default_function",
    binds: Optional[Mapping[tensor.Tensor, Buffer]] = None,
):
    """Build a function with arguments as signature. Code will be generated
    for devices coupled with target information.

    Parameters
    ----------
    inputs : Union[tvm.te.schedule.Schedule, tvm.tir.PrimFunc, IRModule, Mapping[str, IRModule]]
        The input to be built

    args : Optional[List[Union[tvm.tir.Buffer, tensor.Tensor, Var]]]
        The argument lists to the function.

    target : Optional[Union[str, Target]]
        The target and option of the compilation.

    target_host : Optional[Union[str, Target]]
        Host compilation target, if target is device.
        When TVM compiles device specific program such as CUDA,
        we also need host(CPU) side code to interact with the driver
        setup the dimensions and parameters correctly.
        target_host is used to specify the host side codegen target.
        By default, llvm is used if it is enabled,
        otherwise a stackvm intepreter is used.

    name : Optional[str]
        The name of result function.

    binds : Optional[Mapping[tensor.Tensor, tvm.tir.Buffer]]
        Dictionary that maps the binding of symbolic buffer to Tensor.
        By default, a new buffer is created for each tensor in the argument.

    Returns
    -------
    ret : tvm.module
        A module that combines both host and device code.

    Examples
    ________
    There are two typical example uses of this function depending on the type
    of the argument `inputs`:
    1. it is an IRModule.

    .. code-block:: python

        n = 2
        A = te.placeholder((n,), name='A')
        B = te.placeholder((n,), name='B')
        C = te.compute(A.shape, lambda *i: A(*i) + B(*i), name='C')
        s = tvm.te.create_schedule(C.op)
        m = tvm.lower(s, [A, B, C], name="test_add")
        rt_mod = tvm.build(m, target="llvm")

    2. it is a dict of compilation target to IRModule.

    .. code-block:: python

        n = 2
        A = te.placeholder((n,), name='A')
        B = te.placeholder((n,), name='B')
        C = te.compute(A.shape, lambda *i: A(*i) + B(*i), name='C')
        s1 = tvm.te.create_schedule(C.op)
        with tvm.target.cuda() as cuda_tgt:
          s2 = topi.cuda.schedule_injective(cuda_tgt, [C])
          m1 = tvm.lower(s1, [A, B, C], name="test_add1")
          m2 = tvm.lower(s2, [A, B, C], name="test_add2")
          rt_mod = tvm.build({"llvm": m1, "cuda": m2}, target_host="llvm")

    Note
    ----
    See the note on :any:`tvm.target` on target string format.
    """
    if isinstance(inputs, schedule.Schedule):
        if args is None:
            raise ValueError("args must be given for build from schedule")
        input_mod = lower(inputs, args, name=name, binds=binds)
    elif isinstance(inputs, (list, tuple, container.Array)):
        merged_mod = tvm.IRModule({})
        for x in inputs:
            merged_mod.update(lower(x))
        input_mod = merged_mod
    elif isinstance(inputs, (tvm.IRModule, PrimFunc)):
        input_mod = lower(inputs)
    elif not isinstance(inputs, (dict, container.Map)):
        raise ValueError(
            f"Inputs must be Schedule, IRModule or dict of target to IRModule, "
            f"but got {type(inputs)}."
        )

    if not isinstance(inputs, (dict, container.Map)):
        target = Target.current() if target is None else target
        target = target if target else "llvm"
        target_input_mod = {target: input_mod}
    else:
        target_input_mod = inputs

    for tar, mod in target_input_mod.items():
        if not isinstance(tar, (str, Target)):
            raise ValueError("The key of inputs must be str or " "Target when inputs is dict.")
        if not isinstance(mod, tvm.IRModule):
            raise ValueError("inputs must be Schedule, IRModule," "or dict of str to IRModule.")

    target_input_mod, target_host = Target.check_and_update_host_consist(
        target_input_mod, target_host
    )

    if not target_host:
        for tar, mod in target_input_mod.items():
            tar = Target(tar)
            device_type = ndarray.device(tar.kind.name, 0).device_type
            if device_type == ndarray.cpu(0).device_type:
                target_host = tar
                break
    if not target_host:
        target_host = "llvm" if tvm.runtime.enabled("llvm") else "stackvm"

    target_input_mod, target_host = Target.check_and_update_host_consist(
        target_input_mod, target_host
    )

    mod_host_all = tvm.IRModule({})

    device_modules = []
    for tar, input_mod in target_input_mod.items():
        mod_host, mdev = _build_for_device(input_mod, tar, target_host)
        mod_host_all.update(mod_host)
        device_modules.append(mdev)

    # Generate a unified host module.
    rt_mod_host = codegen.build_module(mod_host_all, target_host)

    # Import all modules.
    for mdev in device_modules:
        if mdev:
            rt_mod_host.import_module(mdev)

    if not isinstance(target_host, Target):
        target_host = Target(target_host)
    if (
        target_host.attrs.get("runtime", tvm.runtime.String("c++")) == "c"
        and target_host.attrs.get("system-lib", 0) == 1
    ):
        if target_host.kind.name == "c":
            create_csource_crt_metadata_module = tvm._ffi.get_global_func(
                "runtime.CreateCSourceCrtMetadataModule"
            )
            to_return = create_csource_crt_metadata_module([rt_mod_host], target_host)

        elif target_host.kind.name == "llvm":
            create_llvm_crt_metadata_module = tvm._ffi.get_global_func(
                "runtime.CreateLLVMCrtMetadataModule"
            )
            to_return = create_llvm_crt_metadata_module([rt_mod_host], target_host)
    else:
        to_return = rt_mod_host

    return OperatorModule.from_module(to_return, ir_module_by_target=target_input_mod, name=name)