chore: Fix linting

Signed-off-by: Dheeraj Peri <[email protected]>

Author: peri044

core/partitioning/partitioning.cpp

@@ -160,7 +160,8 @@ void resolveNonTensorInputs(PartitionedGraph& segmented_blocks) { // , std::shar
       }
     }
 
-    // For each non-tensor value in the usage_counts map, keep updating the produce_id to the earliest segmented block that has/produces it.
+    // For each non-tensor value in the usage_counts map, keep updating the produce_id to the earliest segmented block
+    // that has/produces it.
     for (auto& use : usage_counts) {
       // Set the produce_id to the segmented block index that contains/produces this non-tensor torch::jit::Value
       if (segmented_blocks[i].contain_raw_value(use.first)) {
@@ -169,7 +170,6 @@ void resolveNonTensorInputs(PartitionedGraph& segmented_blocks) { // , std::shar
     }
   }
 
-
   std::unordered_set<int> updated_segments;
   for (auto& use : usage_counts) {
     auto use_info = use.second;
@@ -316,7 +316,6 @@ std::vector<SegmentedBlock> segment_graph(torch::jit::Block* block, const Partit
         segmented_blocks.emplace_back(SegmentedBlock::kTorch, std::vector<torch::jit::Node*>{n});
         continue;
       } else if (n->kind() == torch::jit::prim::Loop) {
-
         if (!pytorch_nodes.empty()) {
           segmented_blocks.emplace_back(SegmentedBlock::kTorch, pytorch_nodes);
           pytorch_nodes.clear();

core/partitioning/shape_analysis.cpp

@@ -56,7 +56,9 @@ void getSegmentsOutputByRunning(
   for (auto& input : seg_block.raw_inputs()) {
     TRTORCH_CHECK(
         ivalues_maps.count(input),
-        "Could not find torch::jit::Value* " << input->debugName() << " produced from " << util::node_info(input->node()) << " in lowering graph for mini graph input.\n");
+        "Could not find torch::jit::Value* " << input->debugName() << " produced from "
+                                             << util::node_info(input->node())
+                                             << " in lowering graph for mini graph input.\n");
     if (input->node()->kind() == torch::jit::prim::Param) {
       jit_inputs_ivalues.push_back(ivalues_maps[input]);
     } else if (input->type()->isSubtypeOf(torch::jit::TensorType::get())) {