def METHOD_NAME(self, ctx):
    """In this initialize function, the HF large model is loaded and
    partitioned into multiple stages each on one device using PiPPy.
    Args:
        ctx (context): It is a JSON Object containing information
        pertaining to the model artifacts parameters.
    """
    super().METHOD_NAME(ctx)
    self.manifest = ctx.manifest
    properties = ctx.system_properties
    model_dir = properties.get("model_dir")
    self.device = self.local_rank
    model_path = ctx.model_yaml_config["handler"]["model_path"]
    seed = ctx.model_yaml_config["handler"]["manual_seed"]
    dtype_str = ctx.model_yaml_config["handler"]["dtype"]
    torch.manual_seed(seed)
    dtypes = {"fp32": torch.float32, "fp16": torch.float16, "bf16": torch.bfloat16}
    dtype = dtypes.get(dtype_str, torch.float32)
    if dtype != torch.float32 and dtype_str not in dtypes:
        logger.info(
            f"Unsupported data type {dtype_str}, "
            "please submit a PR to support it. Falling back to fp32 now."
        )
    skip_init_start = time.perf_counter()
    with torch.device("meta"):
        self.model = AutoModelForCausalLM.from_pretrained(
            model_path, use_cache=False, torch_dtype=dtype
        )
    skip_init_end = time.perf_counter()
    logger.info(
        f" init model time on meta device took {skip_init_end - skip_init_start} seconds"
    )
    self.tokenizer = AutoTokenizer.from_pretrained(model_path, return_tensors="pt")
    self.tokenizer.pad_token = self.tokenizer.eos_token
    self.max_length = ctx.model_yaml_config["handler"]["max_length"]
    self.max_new_tokens = ctx.model_yaml_config["handler"]["max_new_tokens"]
    logger.info("Instantiating model Pipeline")
    pippy_compile_time_start = time.perf_counter()
    self.model = get_pipeline_driver(self.model, self.world_size, ctx)
    pippy_compile_time_end = time.perf_counter()
    logger.info(
        f" pippy compile time took {pippy_compile_time_end- pippy_compile_time_start} seconds on rank {self.local_rank}"
    )
    logger.info("Transformer model from path %s loaded successfully", model_dir)
    self.initialized = True