AND	$7, R4, R8
	BEQ	R8, words
	MOVB	(R5), R7
	ADDV	$1, R5
	MOVB	R7, (R4)
	ADDV	$1, R4
	JMP	-6(PC)

words:
	// do 8 bytes at a time if there is room
	ADDV	$-7, R9, R6 // R6 is end pointer-7

	PCALIGN	$16
	SGTU	R6, R4, R8
	BEQ	R8, out
	MOVV	(R5), R7
	ADDV	$8, R5
	MOVV	R7, (R4)
	ADDV	$8, R4
	JMP	-6(PC)

out:
	BEQ	R4, R9, done
	MOVB	(R5), R7
	ADDV	$1, R5
	MOVB	R7, (R4)

    }

    llvm::SourceMgr source_mgr;
    source_mgr.AddNewSourceBuffer(std::move(file), llvm::SMLoc());
    auto module = parseSourceFile<ModuleOp>(source_mgr, context);
    if (module->getOperation() == nullptr) {
      return absl::AbortedError("Failed to parse input MLIR model.");
    }

    return quant::stablehlo::ImportedMlirModuleOp(std::move(module), nullptr);
  }

    SessionOptions options;
    std::vector<std::unique_ptr<Device>> devices;
    Status s = device_factory->CreateDevices(
        options, "/job:worker/replica:0/task:0", &devices);
    device_ = std::move(devices[0]);

    AllocatorAttributes host_alloc_attr;
    host_alloc_attr.set_on_host(true);
    host_allocator_ = device_->GetAllocator(host_alloc_attr);

    AllocatorAttributes device_alloc_attr;

    return absl::InternalError(absl::StrCat("Failed to import SavedModel: ",
                                            module_op.status().ToString()));
  }

  return std::make_pair(std::move(*module_op), std::move(bundle));
}

absl::StatusOr<absl::flat_hash_map<FunctionName, FunctionAlias>>
GetFunctionAliases(absl::string_view saved_model_path,
                   const std::unordered_set<std::string>& tags) {

	MOVV	8(R4), R16
	MOVV	8(R6), R17
	ADDV	$16, R4
	ADDV	$16, R6
	SUBVU	$1, R14
	BEQ	R16, R17, chunk16_loop
	SUBV	$8, R4
	SUBV	$8, R6

byte_loop:
	BEQ	R4, R12, samebytes
	MOVBU	(R4), R8
	ADDVU	$1, R4
	MOVBU	(R6), R9
	ADDVU	$1, R6
	BEQ	R8, R9, byte_loop

byte_cmp:
	SGTU	R8, R9, R4 // R12 = 1 if (R8 > R9)
	BNE	R0, R4, ret
	MOVV	$-1, R4
	JMP	ret

samebytes:
	SGTU	R5, R7, R8

			// The current succeeded interval is behind, so we can move to the next.
			succeededPointer++
		} else if succeededIndexes[succeededPointer].Last > ruleIndexes[rulePointer].Last {
			// The current rule interval is behind, so we can move to the next.
			rulePointer++
		} else {
			// Both intervals end at the same position, we can move to the next succeeded, and next rule.
			succeededPointer++
			rulePointer++
		}

      py_function_lib_(ABSL_DIE_IF_NULL(py_function_lib)),  // Crash OK
      src_saved_model_path_(src_saved_model_path),
      function_aliases_(std::move(function_aliases)),
      tags_(std::move(tags)),
      signature_def_map_(std::move(signature_def_map)),
      signature_keys_(std::move(signature_keys)) {}

absl::Status CalibrationComponent::ExportToSavedModel(
    ModuleOp module_op, absl::string_view calibration_data_dir,

      std::vector<uint8_t>(expected_value.begin(), expected_value.end());
  input_model.buffers.push_back(std::move(model_metadata_buffer));
  auto metadata_t = std::make_unique<tflite::MetadataT>();
  metadata_t->name = tflite::optimize::kTfLiteReducedPrecisionKey;
  metadata_t->buffer = input_model.buffers.size() - 1;
  input_model.metadata.push_back(std::move(metadata_t));

  // Sparsify and create output model
  flatbuffers::FlatBufferBuilder output_builder;

  if (av_tensor == nullptr) {
    TF_ASSIGN_OR_RETURN(
        *tensor, MakeTensorFromPjRtBuffer(tensor->dtype(), tensor->shape(),
                                          std::move(buffer)));
  } else {
    av_tensor->SetBuffer(std::move(buffer));
  }
  return absl::OkStatus();
}

absl::StatusOr<xla::PjRtCApiClient*> GetPjRtCApiClient(
    const DeviceType& device_type) {

        where:
        method     | registration
        'instance' | { ScopedServiceRegistry it -> it.add(new BuildTreeScopedService()) }