code: def get_pipeline_driver(model, world_size, ctx):
      """Returns a pipeline driver for the given model.
      Args:
          model (torch.nn.Module): The model to pipeline.
          world_size (int): The number of pipeline stages.
          ctx (Context): The context containing configuration information.
      Returns:
          torch.nn.Sequential: The pipeline driver for the model.
      """
      # Extract configuration parameters from the context
  
      # Check that the "pippy" and "handler" keys are present in the YAML config
      assert (
          "pippy" in ctx.model_yaml_config
      ), "Missing 'pippy' key in YAML config"
      assert (
          "handler" in ctx.model_yaml_config
      ), "Missing 'handler' key in YAML config"
  
      # Check that the required keys are present in the "pippy" section
  
      assert (
          "input_names" in ctx.model_yaml_config["pippy"]
      ), "Missing 'input_names' key in YAML config"
      assert (
          "model_type" in ctx.model_yaml_config["pippy"]
      ), "Missing 'model_type' key in YAML config"
  
      # Check that the required keys are present in the "handler" section
      assert (
          "model_path" in ctx.model_yaml_config["handler"]
      ), "Missing 'model_path' key in YAML config"
  
      # Set variables from the config
  
      input_names = ctx.model_yaml_config["pippy"]["input_names"]
      model_type = ctx.model_yaml_config["pippy"]["model_type"]
      model_path = ctx.model_yaml_config["handler"]["model_path"]
      try:
          chunks = ctx.model_yaml_config["pippy"]["chunks"]
      except KeyError:
          chunks = 1
      try:
          index_filename = ctx.model_yaml_config["handler"][
              "index_filename"
          ]
      except KeyError:
          index_filename = None
  
      # Check that the index file exists
      if index_filename is not None:
          index_file_path = os.path.join(model_path, index_filename)
          assert os.path.exists(
              index_file_path
          ), f"Index file '{index_file_path}' not found"
      else:
          index_file_path = None
  
      checkpoint_prefix = None
      # Set the model to evaluation mode
      model.eval()
  
      # Extract the concrete arguments for the model's forward method
      sig = inspect.signature(model.forward)
      concrete_args = {
          p.name: p.default
          for p in sig.parameters.values()
          if p.name not in input_names
      }
  
      logger.info("Initializing the model pipeline")
  
      # Create a tracer if necessary
      tracer = PiPPyHFTracer() if model_type == "HF" else None
  
      # Add deprecated_arguments to concrete_args if necessary
      if model_type == "HF" and "bloom" in str(model.__class__):
          concrete_args.setdefault("deprecated_arguments", {})
  
      # Compile the pipeline using PiPPy
      split_policy = split_into_equal_size(world_size)
      pipe_driver, stage_mode = pippy.all_compile(
          model,
          num_ranks=world_size,
          num_chunks=chunks,
          schedule="FillDrain",
          split_policy=split_policy,
          tracer=tracer,
          concrete_args=concrete_args,
          index_filename=index_file_path,
          checkpoint_prefix=checkpoint_prefix,
      )
  
      # Inject the pipeline forward method if necessary
      if model_type == "HF":
          inject_pipeline_forward(model, pipe_driver)
          return model
      else:
          return pipe_driver
  
file_path: serve/ts/handler_utils/distributed/pt_pippy.py
imports: import importlib
  import inspect
  import logging
  import os
  import pippy
  import torch
  import torch.distributed.rpc
label_desc: Use image node_id 2 for calling a global function with example usage: get_pipeline_driver(model, world_size, ctx) and returns: model, pipe_driver