diff --git a/core/conversion/converters/impl/interpolate.cpp b/core/conversion/converters/impl/interpolate.cpp
index 6d83b1f8f1..3adcd42a56 100755
--- a/core/conversion/converters/impl/interpolate.cpp
+++ b/core/conversion/converters/impl/interpolate.cpp
@@ -15,24 +15,34 @@ auto interpolate_registrations = RegisterNodeConversionPatterns()
     .pattern({
         "aten::upsample_nearest1d(Tensor self, int[1] output_size, float? scales=None) -> (Tensor)",
         [](ConversionCtx* ctx, const torch::jit::Node*n, args& args) -> bool {
-            TRTORCH_ASSERT(args[0].IValue()->isTensor(), "Input expected to be of type Tensor");
+            std::cout << "GOT IN HERE!!!!!!" << std::endl;
 
             auto in = args[0].ITensor();
             auto in_shape = util::toVec(in->getDimensions());
-
+                
             // Case 1: user uses output size and not scales
             if (!args[1].IValue()->isNone() && args[2].IValue()->isNone()) {
-                auto output_size = util::toDims(args[1].unwrapToIntList());
+                auto out_size = util::toVec(util::toDims(args[1].unwrapToIntList()));
 
-                TRTORCH_ASSERT(output_size.nbDims == 1, "aten::upsample_nearest1d input Tensor and output size dimension mismatch");
+                TRTORCH_ASSERT(out_size.size() == 1, "aten::upsample_nearest1d input Tensor and output size dimension mismatch");
+                
+                auto out_shape = in_shape;
+                std::copy(out_size.begin(), out_size.end(), out_shape.begin() + (in_shape.size() - out_size.size()));
+                
+                // remove padding that TensorRT adds automatically
+                // out_shape.erase(out_shape.begin(), out_shape.begin()+1);
 
                 auto resize_layer = ctx->net->addResize(*in);
                 TRTORCH_CHECK(resize_layer, "Unable to create interpolation (resizing) layer from node" << *n);
 
-                resize_layer->setOutputDimensions(output_size);
+                resize_layer->setOutputDimensions(util::toDims(out_shape));
                 resize_layer->setResizeMode(nvinfer1::ResizeMode::kNEAREST);
+                resize_layer->setName(util::node_info(n).c_str());
+
+                auto layer_output = ctx->AssociateValueAndTensor(n->outputs()[0], resize_layer->getOutput(0));
+                LOG_DEBUG("Output tensor shape: " << layer_output->getDimensions());
             } else {
-                LOG_DEBUG("scale factor parameters not supported yet.");
+                LOG_DEBUG("scale factor parameter not supported yet.");
             }
 
             return true;
@@ -40,24 +50,27 @@ auto interpolate_registrations = RegisterNodeConversionPatterns()
     }).pattern({
         "aten::upsample_nearest2d(Tensor self, int[2] output_size, float? scales_h=None, float? scales_w=None) -> (Tensor)",
         [](ConversionCtx* ctx, const torch::jit::Node* n, args& args) -> bool {
-            // std::raise(SIGINT);
-
-            TRTORCH_ASSERT(args[0].IValue()->isTensor(), "Input expected to be of type Tensor");
-
             auto in = args[0].ITensor();
             auto in_shape = util::toVec(in->getDimensions());
 
             // Case 1: user uses output_size and not scales_h, scales_w
             if (!args[1].IValue()->isNone() && args[2].IValue()->isNone() && args[3].IValue()->isNone()){
-                auto output_size = util::toDims(args[1].unwrapToIntList());
-
-                TRTORCH_ASSERT( (output_size.nbDims == 1 || output_size.nbDims == 2), "aten::upsample_nearest2d input Tensor and output size dimension mismatch");
+                auto out_size = util::toVec(util::toDims(args[1].unwrapToIntList()));
 
+                TRTORCH_ASSERT(out_size.size() == 2, "aten::upsample_nearest2d input Tensor and output size dimension mismatch");
+                
+                auto out_shape = in_shape;
+                std::copy(out_size.begin(), out_size.end(), out_shape.begin() + (in_shape.size() - out_size.size()));
+                
                 auto resize_layer = ctx->net->addResize(*in);
                 TRTORCH_CHECK(resize_layer, "Unable to create interpolation (resizing) layer from node" << *n);
 
-                resize_layer->setOutputDimensions(output_size);
+                resize_layer->setOutputDimensions(util::toDims(out_shape));
                 resize_layer->setResizeMode(nvinfer1::ResizeMode::kNEAREST);
+                resize_layer->setName(util::node_info(n).c_str());
+
+                auto layer_output = ctx->AssociateValueAndTensor(n->outputs()[0], resize_layer->getOutput(0));
+                LOG_DEBUG("Output tensor shape: " << layer_output->getDimensions());
             } else {
                 LOG_DEBUG("scale factor parameters not supported yet.");
             }
@@ -67,22 +80,27 @@ auto interpolate_registrations = RegisterNodeConversionPatterns()
     }).pattern({
         "aten::upsample_nearest3d(Tensor self, int[3] output_size, float? scales_d=None, float? scales_h=None, float? scales_w=None) -> (Tensor)",
         [](ConversionCtx* ctx, const torch::jit::Node*n, args& args) -> bool {
-            TRTORCH_ASSERT(args[0].IValue()->isTensor(), "Input expected to be of type Tensor");
-
             auto in = args[0].ITensor();
             auto in_shape = util::toVec(in->getDimensions());
 
             // Case 1: user uses output size and not scales_d, scales_h, scales_w
             if (!args[1].IValue()->isNone() && args[2].IValue()->isNone() && args[3].IValue()->isNone() && args[4].IValue()->isNone()) {
-                auto output_size = util::toDims(args[1].unwrapToIntList());
+                auto out_size = util::toVec(util::toDims(args[1].unwrapToIntList()));
 
-                TRTORCH_ASSERT( (output_size.nbDims == 1 || output_size.nbDims == 3), "aten::upsample_nearest3d input Tensor and output size dimension mismatch");
+                TRTORCH_ASSERT(out_size.size() == 3, "aten::upsample_nearest3d input Tensor and output size dimension mismatch");
+                
+                auto out_shape = in_shape;
+                std::copy(out_size.begin(), out_size.end(), out_shape.begin() + (in_shape.size() - out_size.size()));
                 
                 auto resize_layer = ctx->net->addResize(*in);
                 TRTORCH_CHECK(resize_layer, "Unable to create interpolation (resizing) layer from node" << *n);
 
-                resize_layer->setOutputDimensions(output_size);
+                resize_layer->setOutputDimensions(util::toDims(out_shape));
                 resize_layer->setResizeMode(nvinfer1::ResizeMode::kNEAREST);
+                resize_layer->setName(util::node_info(n).c_str());
+
+                auto layer_output = ctx->AssociateValueAndTensor(n->outputs()[0], resize_layer->getOutput(0));
+                LOG_DEBUG("Output tensor shape: " << layer_output->getDimensions());
             } else {
                 LOG_DEBUG("scale factor parameters not supported yet.");
             }