Fix -Wunused-variable warnings

Remove unused variables. Add __unused attribute to variable used in assert clause. Move variable to inside header guard when only used in header guard.

PiperOrigin-RevId: 481699164

mediapipe/calculators/tensor/image_to_tensor_converter_metal.cc

@@ -262,7 +262,6 @@ class SubRectExtractorMetal {
     RET_CHECK(pipeline_state != nil);
 
     std::string output_type_def;
-    MTLPixelFormat pixel_format;
     switch (output_format) {
       case OutputFormat::kF16C4:
         output_type_def = R"(

mediapipe/calculators/tensor/inference_calculator_metal.cc

@@ -224,9 +224,6 @@ absl::Status InferenceCalculatorMetalImpl::InitInterpreter(
 
 void InferenceCalculatorMetalImpl::AddDelegate(
     CalculatorContext* cc, tflite::InterpreterBuilder* interpreter_builder) {
-  const auto& calculator_opts =
-      cc->Options<mediapipe::InferenceCalculatorOptions>();
-
   // Configure and create the delegate.
   TFLGpuDelegateOptions options;
   // `enable_quantization` enables the run of sparse models i.e. the models with

mediapipe/calculators/tensor/tensor_converter_calculator.cc

@@ -296,7 +296,6 @@ absl::Status TensorConverterCalculator::ProcessGPU(CalculatorContext* cc) {
   output_tensors->emplace_back(Tensor::ElementType::kFloat32,
                                Tensor::Shape{1, height, width, channels});
 #if MEDIAPIPE_METAL_ENABLED
-  id<MTLDevice> device = gpu_helper_.mtlDevice;
   id<MTLCommandBuffer> command_buffer = [gpu_helper_ commandBuffer];
   command_buffer.label = @"TensorConverterCalculatorConvert";
   id<MTLComputeCommandEncoder> compute_encoder =

mediapipe/calculators/tensor/tensors_to_detections_calculator.cc

@@ -532,7 +532,6 @@ absl::Status TensorsToDetectionsCalculator::ProcessGPU(
                                          detection_classes.data(),
                                          output_detections));
 #elif MEDIAPIPE_METAL_ENABLED
-  id<MTLDevice> device = gpu_helper_.mtlDevice;
   if (!anchors_init_) {
     if (input_tensors.size() == kNumInputTensorsWithAnchors) {
       RET_CHECK_EQ(input_tensors.size(), kNumInputTensorsWithAnchors);