code: def benchmark(self, args):
      flush()
  
      print(
          f"[INFO] {self.pipe.__class__.__name__}: Running benchmark with: {vars(args)}\n"
      )
  
      time = benchmark_fn(
          self.run_inference, self.pipe, args
      )  # in seconds.
      memory = bytes_to_giga_bytes(
          torch.cuda.max_memory_allocated()
      )  # in GBs.
      benchmark_info = BenchmarkInfo(time=time, memory=memory)
  
      pipeline_class_name = str(self.pipe.__class__.__name__)
      flush()
      csv_dict = generate_csv_dict(
          pipeline_cls=pipeline_class_name,
          ckpt=self.lora_id,
          args=args,
          benchmark_info=benchmark_info,
      )
      filepath = self.get_result_filepath(args)
      write_to_csv(filepath, csv_dict)
      print(f"Logs written to: {filepath}")
      flush()
  
imports: import os
  import sys
  import torch
  from diffusers import AutoPipelineForImage2Image, AutoPipelineForInpainting, AutoPipelineForText2Image, ControlNetModel, LCMScheduler, StableDiffusionAdapterPipeline, StableDiffusionControlNetPipeline, StableDiffusionXLAdapterPipeline, StableDiffusionXLControlNetPipeline, T2IAdapter, WuerstchenCombinedPipeline
  from diffusers.utils import load_image
  from utils import BASE_PATH, PROMPT, BenchmarkInfo, benchmark_fn, bytes_to_giga_bytes, flush, generate_csv_dict, write_to_csv
label_desc: Use image node_id 4 for calling the LCMLoRATextToImageBenchmark obj's underlying member method code with example usage: obj.benchmark(args) without return types