diff --git a/py/torch_tensorrt/dynamo/conversion/impl/conv.py b/py/torch_tensorrt/dynamo/conversion/impl/conv.py
index 285da2a04c..ff7deb0962 100644
--- a/py/torch_tensorrt/dynamo/conversion/impl/conv.py
+++ b/py/torch_tensorrt/dynamo/conversion/impl/conv.py
@@ -6,7 +6,7 @@
 import tensorrt as trt
 import torch
 from torch.fx.node import Target
-from torch_tensorrt.dynamo.conversion import aten_ops_converters
+from torch_tensorrt.dynamo.conversion import impl
 from torch_tensorrt.dynamo.conversion.converter_utils import extend_attr_to_tuple
 from torch_tensorrt.fx.converters.converter_utils import (
     SourceIR,
@@ -41,8 +41,8 @@ def convNd(
 
     if is_conv1d:
         # Apply an unsqueeze operation to transform the conv1d problem into conv2d
-        input = aten_ops_converters.aten_ops_unsqueeze(
-            network, target, (input, -1), {}, name + "_unsqueeze"
+        input = impl.unsqueeze.unsqueeze(
+            network, target, source_ir, name + "_unsqueeze_conv1d", input, -1
         )
 
     # Process bias terms
@@ -63,8 +63,8 @@ def convNd(
         weight = get_trt_tensor(network, weight, f"{name}_weight")
         # Append new dimension (unsqueeze) if the convolution is 1d
         if is_conv1d:
-            weight = aten_ops_converters.aten_ops_unsqueeze(
-                network, target, (weight, -1), {}, name + "_unsqueeze_weight"
+            input = impl.unsqueeze.unsqueeze(
+                network, target, source_ir, name + "_unsqueeze_weight", weight, -1
             )
 
     elif isinstance(weight, (torch.Tensor, np.ndarray)):
@@ -122,8 +122,8 @@ def convNd(
 
     if is_conv1d:
         # Apply a squeeze operation to transform the conv2d problem back into conv1d
-        result = aten_ops_converters.aten_ops_squeeze(
-            network, target, (result, -1), {}, name + "_squeeze"
+        result = impl.squeeze.squeeze(
+            network, target, source_ir, name + "_squeeze_conv1d", result, -1
         )
 
     return result