code: def serve(
      self, blocking=False, debug=False, port=None, bind_all=False
  ):
      context = _get_context()
      if context == _CONTEXT_COLAB:
          return self._serve_colab(
              blocking=blocking, debug=debug, port=port
          )
      else:
          return self._serve(
              blocking=blocking,
              debug=debug,
              port=port,
              bind_all=bind_all,
          )
  
imports: from collections import namedtuple
  from itertools import cycle
  from typing import Any, Callable, Dict, Iterable, List, NamedTuple, Optional, Tuple, Union
  import torch
  from captum.attr import IntegratedGradients
  from captum.attr._utils.batching import _batched_generator
  from captum.insights.attr_vis.attribution_calculation import AttributionCalculation, OutputScore
  from captum.insights.attr_vis.config import ATTRIBUTION_METHOD_CONFIG, ATTRIBUTION_NAMES_TO_METHODS
  from captum.insights.attr_vis.features import BaseFeature
  from captum.insights.attr_vis.server import namedtuple_to_dict
  from captum.log import log_usage
  from torch import Tensor
  from torch.nn import Module
label_desc: Use image node_id 5 for calling the AttributionVisualizer obj's underlying member method code with example usage: obj.serve(blocking, debug, port, bind_all) and returns: self, self