diff --git a/ethosu/vela/operation.py b/ethosu/vela/operation.py
index 277f2de..5a6423d 100644
--- a/ethosu/vela/operation.py
+++ b/ethosu/vela/operation.py
@@ -15,6 +15,9 @@
 # limitations under the License.
 # Description:
 # Internal representation of a Neural Network Operation.
+# For Class name forward references for the type annotations. (see PEP 563).
+from __future__ import annotations
+
 import copy
 from collections import namedtuple
 from enum import Enum
@@ -24,13 +27,14 @@
 from typing import Optional
 from typing import Tuple
 from typing import TYPE_CHECKING
+from typing import Union
 
 from .api import NpuRoundingMode
 from .errors import VelaError
 from .numeric_util import full_shape
 from .shape4d import Shape4D
 
-
+# Import needed for Type annotations. Only import for Type checking to avoid run-time errors due to cyclic import.
 if TYPE_CHECKING:
     from .tensor import Tensor
 
@@ -80,9 +84,6 @@
     def area_height(self) -> int:
         return (self.height - 1) * self.dilation.y + 1
 
-    def dilation(self) -> PointXY:
-        return self.dilation
-
     def dilated_wh(self) -> Tuple[int, int]:
         """Returns the dilated kernel width/height"""
         return self.dilation.x * (self.width - 1) + 1, self.dilation.y * (self.height - 1) + 1
@@ -443,7 +444,7 @@
     return act
 
 
-def get_slice_offsets(input_shape: List[int], offset_tens: int, offset_mask: int, is_begin: bool = True):
+def get_slice_offsets(input_shape: List[int], offset_tens: Tensor, offset_mask: int, is_begin: bool = True):
     # For strided slice operator: get start or end offsets
     offsets = len(input_shape) * [0] if is_begin else input_shape[:]
     for idx in range(len(input_shape)):
@@ -493,7 +494,7 @@
         self.type = op_type
         self.name = name
         self.attrs: Dict[str, Any] = {}
-        self.inputs: List[Tensor] = []
+        self.inputs: List[Optional[Tensor]] = []
         self.outputs: List[Tensor] = []
         self.intermediates: List[Tensor] = []
         self.flops = 0
@@ -514,9 +515,9 @@
         self.ofm_shapes: List[Shape4D] = []
         # If not none: contains rescale to be used as output scaling
         # (which overrides the ofm tensor's scale)
-        self.rescale = None
-        self.read_offsets: List[Shape4D] = [None, None]  # offset for [ifm, ifm2]
-        self.read_shapes: List[Shape4D] = [None, None]  # read shape for [ifm, ifm2]
+        self.rescale: Optional[Union[Tuple[int, int], ExplicitScaling]] = None
+        self.read_offsets: List[Optional[Shape4D]] = [None, None]  # offset for [ifm, ifm2]
+        self.read_shapes: List[Optional[Shape4D]] = [None, None]  # read shape for [ifm, ifm2]
         self.rounding_mode: Optional[NpuRoundingMode] = None
         # Rescale op in TOSA supplies explicit multiplier and shift values
         self.explicit_scaling: Optional[ExplicitScaling] = None