diff --git a/hdk/common/representation/intermediate.py b/hdk/common/representation/intermediate.py
index ba60f8c23..fe3610e63 100644
--- a/hdk/common/representation/intermediate.py
+++ b/hdk/common/representation/intermediate.py
@@ -11,16 +11,20 @@ from ..data_types.dtypes_helpers import (
     mix_scalar_values_determine_holding_dtype,
 )
 
+IR_MIX_VALUES_FUNC_ARG_NAME = "mix_values_func"
+
 
 class IntermediateNode(ABC):
     """Abstract Base Class to derive from to represent source program operations."""
 
     inputs: List[BaseValue]
     outputs: List[BaseValue]
+    _n_in: int  # _n_in indicates how many inputs are required to evaluate the IntermediateNode
 
     def __init__(
         self,
         inputs: Iterable[BaseValue],
+        **_kwargs,  # This is to be able to feed arbitrary arguments to IntermediateNodes
     ) -> None:
         self.inputs = list(inputs)
         assert all(isinstance(x, BaseValue) for x in self.inputs)
@@ -28,13 +32,15 @@ class IntermediateNode(ABC):
     def _init_binary(
         self,
         inputs: Iterable[BaseValue],
+        mix_values_func: Callable[..., BaseValue] = mix_scalar_values_determine_holding_dtype,
+        **_kwargs,  # Required to conform to __init__ typing
     ) -> None:
         """__init__ for a binary operation, ie two inputs."""
         IntermediateNode.__init__(self, inputs)
 
         assert len(self.inputs) == 2
 
-        self.outputs = [mix_scalar_values_determine_holding_dtype(self.inputs[0], self.inputs[1])]
+        self.outputs = [mix_values_func(self.inputs[0], self.inputs[1])]
 
     def _is_equivalent_to_binary_commutative(self, other: object) -> bool:
         """is_equivalent_to for a binary and commutative operation."""
@@ -82,10 +88,30 @@ class IntermediateNode(ABC):
             Any: the result of the computation
         """
 
+    @classmethod
+    def n_in(cls) -> int:
+        """Returns how many inputs the node has.
+
+        Returns:
+            int: The number of inputs of the node.
+        """
+        return cls._n_in
+
+    @classmethod
+    def requires_mix_values_func(cls) -> bool:
+        """Function to determine whether the Class requires a mix_values_func to be built.
+
+        Returns:
+            bool: True if __init__ expects a mix_values_func argument.
+        """
+        return cls.n_in() > 1
+
 
 class Add(IntermediateNode):
     """Addition between two values."""
 
+    _n_in: int = 2
+
     __init__ = IntermediateNode._init_binary
     is_equivalent_to = IntermediateNode._is_equivalent_to_binary_commutative
 
@@ -96,6 +122,8 @@ class Add(IntermediateNode):
 class Sub(IntermediateNode):
     """Subtraction between two values."""
 
+    _n_in: int = 2
+
     __init__ = IntermediateNode._init_binary
     is_equivalent_to = IntermediateNode._is_equivalent_to_binary_non_commutative
 
@@ -106,6 +134,8 @@ class Sub(IntermediateNode):
 class Mul(IntermediateNode):
     """Multiplication between two values."""
 
+    _n_in: int = 2
+
     __init__ = IntermediateNode._init_binary
     is_equivalent_to = IntermediateNode._is_equivalent_to_binary_commutative
 
@@ -118,6 +148,7 @@ class Input(IntermediateNode):
 
     input_name: str
     program_input_idx: int
+    _n_in: int = 1
 
     def __init__(
         self,
@@ -147,6 +178,7 @@ class ConstantInput(IntermediateNode):
     """Node representing a constant of the program."""
 
     _constant_data: Any
+    _n_in: int = 0
 
     def __init__(
         self,
@@ -191,6 +223,7 @@ class ArbitraryFunction(IntermediateNode):
     op_args: Tuple[Any, ...]
     op_kwargs: Dict[str, Any]
     op_name: str
+    _n_in: int = 1
 
     def __init__(
         self,
diff --git a/hdk/common/tracing/base_tracer.py b/hdk/common/tracing/base_tracer.py
index e6774147a..2c9018e54 100644
--- a/hdk/common/tracing/base_tracer.py
+++ b/hdk/common/tracing/base_tracer.py
@@ -1,10 +1,11 @@
 """This file holds the code that can be shared between tracers."""
 
 from abc import ABC, abstractmethod
-from typing import Any, Iterable, List, Tuple, Type, Union
+from typing import Any, Callable, Iterable, List, Tuple, Type, Union
 
 from ..data_types import BaseValue
 from ..representation import intermediate as ir
+from ..representation.intermediate import IR_MIX_VALUES_FUNC_ARG_NAME
 
 
 class BaseTracer(ABC):
@@ -13,6 +14,7 @@ class BaseTracer(ABC):
     inputs: List["BaseTracer"]
     traced_computation: ir.IntermediateNode
     output: BaseValue
+    _mix_values_func: Callable[..., BaseValue]
 
     def __init__(
         self,
@@ -47,6 +49,10 @@ class BaseTracer(ABC):
             BaseTracer: The BaseTracer for that constant.
         """
 
+    @classmethod
+    def _get_mix_values_func(cls):
+        return cls._mix_values_func
+
     def instantiate_output_tracers(
         self,
         inputs: Iterable[Union["BaseTracer", Any]],
@@ -71,8 +77,15 @@ class BaseTracer(ABC):
 
         sanitized_inputs = [sanitize(inp) for inp in inputs]
 
+        additional_parameters = (
+            {IR_MIX_VALUES_FUNC_ARG_NAME: self._get_mix_values_func()}
+            if computation_to_trace.requires_mix_values_func()
+            else {}
+        )
+
         traced_computation = computation_to_trace(
             (x.output for x in sanitized_inputs),
+            **additional_parameters,
         )
 
         output_tracers = tuple(
diff --git a/hdk/hnumpy/tracing.py b/hdk/hnumpy/tracing.py
index 075409c38..91a4ac401 100644
--- a/hdk/hnumpy/tracing.py
+++ b/hdk/hnumpy/tracing.py
@@ -7,6 +7,7 @@ import numpy
 from numpy.typing import DTypeLike
 
 from ..common.data_types import BaseValue
+from ..common.data_types.dtypes_helpers import mix_scalar_values_determine_holding_dtype
 from ..common.operator_graph import OPGraph
 from ..common.representation.intermediate import ArbitraryFunction, ConstantInput
 from ..common.tracing import BaseTracer, make_input_tracers, prepare_function_parameters
@@ -30,6 +31,8 @@ NPConstantInput = partial(
 class NPTracer(BaseTracer):
     """Tracer class for numpy operations."""
 
+    _mix_values_func: Callable[..., BaseValue] = mix_scalar_values_determine_holding_dtype
+
     def __array_ufunc__(self, ufunc, method, *input_tracers, **kwargs):
         """Catch calls to numpy ufunc and routes them to tracing functions if supported.