code: def load_model(
      self,
      device,
      model_name,
      batch_size=None,
      part=None,
      extra_args=None,
  ):
      if self.args.enable_activation_checkpointing:
          raise NotImplementedError(
              "Activation checkpointing not implemented for Torchbench models"
          )
      is_training = self.args.training
      use_eval_mode = self.args.use_eval_mode
      dynamic_shapes = self.args.dynamic_shapes
      candidates = [
          f"torchbenchmark.models.{model_name}",
          f"torchbenchmark.canary_models.{model_name}",
          f"torchbenchmark.models.fb.{model_name}",
      ]
      for c in candidates:
          try:
              module = importlib.import_module(c)
              break
          except ModuleNotFoundError as e:
              if e.name != c:
                  raise
      else:
          raise ImportError(f"could not import any of {candidates}")
      benchmark_cls = getattr(module, "Model", None)
      if benchmark_cls is None:
          raise NotImplementedError(f"{model_name}.Model is None")
  
      if not hasattr(benchmark_cls, "name"):
          benchmark_cls.name = model_name
  
      cant_change_batch_size = (
          not getattr(benchmark_cls, "ALLOW_CUSTOMIZE_BSIZE", True)
          or model_name in DONT_CHANGE_BATCH_SIZE
      )
      if cant_change_batch_size:
          batch_size = None
      if (
          batch_size is None
          and is_training
          and model_name in USE_SMALL_BATCH_SIZE
      ):
          batch_size = USE_SMALL_BATCH_SIZE[model_name]
      elif (
          batch_size is None
          and not is_training
          and model_name in INFERENCE_SMALL_BATCH_SIZE
      ):
          batch_size = INFERENCE_SMALL_BATCH_SIZE[model_name]
  
      # Control the memory footprint for few models
      if (
          self.args.accuracy
          and model_name in MAX_BATCH_SIZE_FOR_ACCURACY_CHECK
      ):
          batch_size = min(
              batch_size, MAX_BATCH_SIZE_FOR_ACCURACY_CHECK[model_name]
          )
  
      # workaround "RuntimeError: not allowed to set torch.backends.cudnn flags"
      torch.backends.__allow_nonbracketed_mutation_flag = True
      if extra_args is None:
          extra_args = []
      if part:
          extra_args += ["--part", part]
  
      if model_name == "vision_maskrcnn" and is_training:
          # Output of vision_maskrcnn model is a list of bounding boxes,
          # sorted on the basis of their scores. This makes accuracy
          # comparison hard with torch.compile. torch.compile can cause minor
          # divergences in the output because of how fusion works for amp in
          # TorchInductor compared to eager.  Therefore, instead of looking at
          # all the bounding boxes, we compare only top 4.
          model_kwargs = {"box_detections_per_img": 4}
          benchmark = benchmark_cls(
              test="train",
              device=device,
              batch_size=batch_size,
              extra_args=extra_args,
              model_kwargs=model_kwargs,
          )
      elif is_training:
          benchmark = benchmark_cls(
              test="train",
              device=device,
              batch_size=batch_size,
              extra_args=extra_args,
          )
      else:
          benchmark = benchmark_cls(
              test="eval",
              device=device,
              batch_size=batch_size,
              extra_args=extra_args,
          )
      model, example_inputs = benchmark.get_module()
  
      # Models that must be in train mode while training
      if is_training and (
          not use_eval_mode or model_name in ONLY_TRAINING_MODE
      ):
          model.train()
      else:
          model.eval()
      gc.collect()
      batch_size = benchmark.batch_size
  
      # Torchbench has quite different setup for yolov3, so directly passing
      # the right example_inputs
      if model_name == "yolov3":
          example_inputs = (
              torch.rand(batch_size, 3, 384, 512).to(device),
          )
      # See https://github.com/pytorch/benchmark/issues/1561
      if model_name == "maml_omniglot":
          batch_size = 5
          assert example_inputs[0].shape[0] == batch_size
      if model_name == "vision_maskrcnn":
          batch_size = 1
      # global current_name, current_device
      # current_device = device
      # current_name = benchmark.name
  
      if self.args.trace_on_xla:
          # work around for: https://github.com/pytorch/xla/issues/4174
          import torch_xla  # noqa: F401
      self.validate_model(model, example_inputs)
      return device, benchmark.name, model, example_inputs, batch_size
  
imports: import gc
  import importlib
  import logging
  import os
  import re
  import sys
  import warnings
  from os.path import abspath, exists
  import torch
  from torch._dynamo.testing import collect_results, reduce_to_scalar_loss
  from torch._dynamo.utils import clone_inputs
label_desc: Use image node_id 16 for calling the TorchBenchmarkRunner obj's underlying member method code with example usage: obj.load_model(device, model_name, batch_size, part, extra_args) and returns: device,  benchmark,  model,  example_inputs,  batch_size