code: def test(
      self,
      model_w_task,
      dtype,
      enable_cuda_graph,
      query,
      inf_kwargs,
      assert_fn,
      invalid_model_task_config,
  ):
      if invalid_model_task_config:
          pytest.skip(invalid_model_task_config)
  
      model, task = model_w_task
      local_rank = int(os.getenv("LOCAL_RANK", "0"))
  
      # Load the model on CPU first to avoid OOM for large models @fp32
      pipe = pipeline(task, model=model, device=-1, framework="pt")
      if dtype == torch.half:
          pipe.model.half()
  
      # Switch device to GPU after converting to half
      device = torch.device(f"cuda:{local_rank}")
      pipe.device = device
      pipe.model.to(device)
  
      # Warm-up queries for perf measurement
      # for i in range(10):
      #    _ = pipe(query, **inf_kwargs)
      torch.cuda.synchronize()
      start = time.time()
      bs_output = pipe(query, **inf_kwargs)
      torch.cuda.synchronize()
      bs_time = time.time() - start
  
      pipe.model = deepspeed.init_inference(
          pipe.model,
          mp_size=1,
          dtype=dtype,
          replace_method="auto",
          replace_with_kernel_inject=True,
          enable_cuda_graph=enable_cuda_graph,
      )
      # Warm-up queries for perf measurement
      # for i in range(10):
      #    _ = pipe(query, **inf_kwargs)
      torch.cuda.synchronize()
      start = time.time()
      ds_output = pipe(query, **inf_kwargs)
      torch.cuda.synchronize()
      ds_time = time.time() - start
  
      # facebook/opt* and some bigscient/bloom* models are not matching
      # baseline exactly, adding an exception to them for now
      if ("opt" in model) or ("bloom" in model):
          bs_output = pipe(query, **inf_kwargs)
  
      # These performance tests are only measuring the time for a single
      # inference request, we just want to check that performance isn't terrible
      # assert ds_time <= (bs_time * 1.1)
      assert assert_fn(bs_output, ds_output)
  
imports: import os
  import time
  import torch
  import pytest
  import itertools
  import deepspeed
  from deepspeed.git_version_info import torch_info
  from unit.common import DistributedTest
  from packaging import version
  from deepspeed.ops.op_builder import OpBuilder
  from transformers import pipeline
  from transformers.models.t5.modeling_t5 import T5Block
  from transformers.models.roberta.modeling_roberta import RobertaLayer
  from huggingface_hub import HfApi
label_desc: Use image node_id 1 for calling the TestModelTask obj's underlying member method code with example usage: obj.test(model_w_task, dtype, enable_cuda_graph, query, inf_kwargs, assert_fn, invalid_model_task_config) without return types