class_docstr: Example helper class for comprehending DeepSpeed Meta Tensors, meant to mimic HF pipelines.
  The DSPipeline can run with and without meta tensors.
code: def __call__(self, inputs=["test"], num_tokens=100, do_sample=False):
      if isinstance(inputs, str):
          input_list = [inputs]
      else:
          input_list = inputs
  
      outputs = self.generate_outputs(
          input_list, num_tokens=num_tokens, do_sample=do_sample
      )
      return outputs
  
file_path: deepspeedexamples/inference/huggingface/text-generation/utils.py
imports: import os
  import io
  from pathlib import Path
  import json
  import deepspeed
  import torch
  from huggingface_hub import snapshot_download
  from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, LlamaTokenizerFast
  from deepspeed.accelerator import get_accelerator
label_desc: Use image node_id 2 for calling the DSPipeline obj's underlying member method code with example usage: obj.__call__(inputs, num_tokens, do_sample) and returns: outputs