more lint fixes

Author: blchu

core/conversion/converters/impl/bitwise.cpp

@@ -9,48 +9,44 @@ namespace conversion {
 namespace converters {
 namespace impl {
 
-
-auto bitwisenot TORCHTRT_UNUSED =
-    RegisterNodeConversionPatterns()
-        .pattern({"aten::bitwise_not(Tensor self) -> Tensor",
-                  [](ConversionCtx* ctx, const torch::jit::Node* n, args& args) -> bool {
-                    auto in = args[0].ITensorOrFreeze(ctx);
-                    nvinfer1::ILayer* out;
-                    
-                    if(in->getType() == nvinfer1::DataType::kINT32) {
-                      // Integer case, using ~x = -x - 1
-                      auto neg_one = torch::tensor({-1}, util::TRTDataTypeToScalarType(in->getType()));
-                      auto neg_one_const = tensor_to_const(ctx, neg_one);
-                      auto neg = add_elementwise(
-                          ctx,
-                          nvinfer1::ElementWiseOperation::kPROD,
-                          in,
-                          neg_one_const,
-                          util::node_info(n) + std::string("_Negation"));
-                      TORCHTRT_CHECK(neg, "Unable to create prod layer from node: " << *n);
-                      out = add_elementwise(
-                          ctx,
-                          nvinfer1::ElementWiseOperation::kSUM,
-                          neg->getOutput(0),
-                          neg_one_const,
-                          util::node_info(n) + std::string("_SubOne"));
-                      TORCHTRT_CHECK(out, "Unable to create sum layer from node: " << *n);
-                    } else if(in->getType() == nvinfer1::DataType::kBOOL) {
-                      // Boolean case
-                      out = ctx->net->addUnary(*in, nvinfer1::UnaryOperation::kNOT);
-                      TORCHTRT_CHECK(out, "Unable to create logical not layer from node: " << *n);
-                    } else {
-                      LOG_ERROR("Input tensor must be 32 bit integer or boolean");
-                      return false;
-                    }
-
-                    out->setName(util::node_info(n).c_str());
-                    auto out_tensor = ctx->AssociateValueAndTensor(n->outputs()[0], out->getOutput(0));
-                    LOG_DEBUG("Output tensor shape: " << out_tensor->getDimensions());
-
-                    return true;
-                  }});
-
+auto bitwise_not_registrations TORCHTRT_UNUSED = RegisterNodeConversionPatterns().pattern(
+    {"aten::bitwise_not(Tensor self) -> Tensor", [](ConversionCtx* ctx, const torch::jit::Node* n, args& args) -> bool {
+       auto in = args[0].ITensorOrFreeze(ctx);
+       nvinfer1::ILayer* out;
+
+       if (in->getType() == nvinfer1::DataType::kINT32) {
+         // Integer case, using ~x = -x - 1
+         auto neg_one = torch::tensor({-1}, util::TRTDataTypeToScalarType(in->getType()));
+         auto neg_one_const = tensor_to_const(ctx, neg_one);
+         auto neg = add_elementwise(
+             ctx,
+             nvinfer1::ElementWiseOperation::kPROD,
+             in,
+             neg_one_const,
+             util::node_info(n) + std::string("_Negation"));
+         TORCHTRT_CHECK(neg, "Unable to create prod layer from node: " << *n);
+         out = add_elementwise(
+             ctx,
+             nvinfer1::ElementWiseOperation::kSUM,
+             neg->getOutput(0),
+             neg_one_const,
+             util::node_info(n) + std::string("_SubOne"));
+         TORCHTRT_CHECK(out, "Unable to create sum layer from node: " << *n);
+       } else if (in->getType() == nvinfer1::DataType::kBOOL) {
+         // Boolean case
+         out = ctx->net->addUnary(*in, nvinfer1::UnaryOperation::kNOT);
+         TORCHTRT_CHECK(out, "Unable to create logical not layer from node: " << *n);
+       } else {
+         LOG_ERROR("Input tensor must be 32 bit integer or boolean");
+         return false;
+       }
+
+       out->setName(util::node_info(n).c_str());
+       auto out_tensor = ctx->AssociateValueAndTensor(n->outputs()[0], out->getOutput(0));
+       LOG_DEBUG("Output tensor shape: " << out_tensor->getDimensions());
+
+       return true;
+     }});
 
 } // namespace impl
 } // namespace converters