code: import re
  import torch
  from ..utils import is_per_tensor, is_per_channel
  
  from torch.fx import GraphModule, map_arg
  
  from torch.fx.graph import (
      Graph,
      Node,
  )
  
  from typing import Callable, Optional, List, Dict, Any
  
  # turn foo.bar -> ['foo', 'bar']
  def _parent_name(target):
      r = target.rsplit('.', 1)
      if len(r) == 1:
          return '', r[0]
      else:
          return r[0], r[1]
  
  def graph_pretty_str(g, shorten=True) -> str:
      """Returns a printable representation of the ops in the graph of g.
      If shorten is True, tries to abbreviate fields.
      """
      built_in_func_re = re.compile('<built-in function (.*)>')
      built_in_meth_re = re.compile('<built-in method (.*) of type.*>')
      op_dict = {
          'placeholder': 'plchdr',
          'get_attr': 'gt_prm',
          'call_function': 'cl_fun',
          'call_module': 'cl_mod',
          'call_method': 'cl_meth',
      }
  
      max_lens = {}
      col_names = ("name", "op", "target", "args", "kwargs")
      for s in col_names:
          max_lens[s] = len(s)
  
      results = []
      for n in g.nodes:
  
          # activation_post_process_0 -> obs_0
          name = str(n.name)
          if shorten:
              name = name.replace("activation_post_process", "obs")
  
          op = str(n.op)
          # placeholder -> plchdr, and so on
          if shorten and op in op_dict:
              op = op_dict[op]
  
          target = str(n.target)
          # <built-in function foo> -> <bi_fun foo>, and so on
          if shorten:
              built_in_func = built_in_func_re.search(target)
              if built_in_func:
                  target = f"<bi_fun {built_in_func.group(1)}>"
              built_in_meth = built_in_meth_re.search(target)
              if built_in_meth:
                  target = f"<bi_meth {built_in_meth.group(1)}>"
              target = target.replace("activation_post_process", "obs")
  
          args = str(n.args)
          if shorten:
              args = args.replace("activation_post_process", "obs")
  
          kwargs = str(n.kwargs)
  
          # calculate maximum length of each column, so we can tabulate properly
          for k, v in zip(col_names, (name, op, target, args, kwargs)):
              max_lens[k] = max(max_lens[k], len(v))
          results.append([name, op, target, args, kwargs])
  
      res_str = ""
      format_str = "{:<{name}} {:<{op}} {:<{target}} {:<{args}} {:<{kwargs}}\n"
      res_str += format_str.format(*col_names, **max_lens)
      for result in results:
          res_str += format_str.format(*result, **max_lens)
  
      # print an exra note on abbreviations which change attribute names,
      # since users will have to un-abbreviate for further debugging
      if shorten:
          res_str += "*obs_{n} = activation_post_process_{n}\n"
      return res_str
  
  def get_per_tensor_qparams(activation_post_process):
      assert is_per_tensor(activation_post_process.qscheme), 'Only per tensor quantization is supported'
      scale, zero_point = activation_post_process.calculate_qparams()
      scale = float(scale)
      zero_point = int(zero_point)
      dtype = activation_post_process.dtype
      return scale, zero_point, dtype
  
  def get_quantize_op_and_qparams(activation_post_process):
      ''' Given an activation_post_process module,
      return quantize op(e.g. quantize_per_tensor) and a dictionary
      of extracted qparams from the module
      '''
      scale, zero_point = activation_post_process.calculate_qparams()
      dtype = activation_post_process.dtype
      if is_per_channel(activation_post_process.qscheme):
          ch_axis = int(activation_post_process.ch_axis)
          qparams = {'_scale_': scale, '_zero_point_': zero_point, '_axis_': ch_axis, '_dtype_': dtype}
          quantize_op = torch.quantize_per_channel
      else:
          scale = float(scale)
          zero_point = int(zero_point)
          qparams = {'_scale_': scale, '_zero_point_': zero_point, '_dtype_': dtype}
          quantize_op = torch.quantize_per_tensor  # type: ignore
      return quantize_op, qparams
  
  def quantize_node(root_module, graph, node, activation_post_process):
      ''' Add quantization nodes for given node to graph
      with the qparams calculated from activation_post_process module
      e.g. Given input `node` in `node = self.conv(x)`, insert node:
      `quantized_node = torch.quantize_per_tensor(x, self._scale_0, self._zer_point_0, self._dtype_0)`
      where self._scale_0, self._zero_point_0 and self._dtype_0 are
      calculated from `activation_post_process`
      '''
      def module_has_qparams_attr_with_index(module, qparams, i):
          for name in qparams.keys():
              if hasattr(module, name + str(i)):
                  return True
          return False
  
      def get_next_qparams_idx(module, qparams):
          idx = 0
          while module_has_qparams_attr_with_index(module, qparams, idx):
              idx += 1
          return idx
  
      quantize_op, qparams = get_quantize_op_and_qparams(activation_post_process)
      idx = get_next_qparams_idx(root_module, qparams)
      inputs = [node]
      for key, value in qparams.items():
          setattr(root_module, key + str(idx), value)
          qparam_full_path = key + str(idx)
          inputs.append(graph.create_node('get_attr', qparam_full_path))
      return graph.create_node('call_function', quantize_op, tuple(inputs), {})
  
  def get_custom_module_class_keys(custom_config_dict, custom_config_dict_key):
      r""" Get all the unique custom module keys in the custom config dict
      e.g.
      Input:
      custom_config_dict = {
          "float_to_observed_custom_module_class": {
             "static": {
                 CustomModule1: ObservedCustomModule
             },
             "dynamic": {
                 CustomModule2: DynamicObservedCustomModule
             },
             "weight_only": {
                 CustomModule3: WeightOnlyObservedCustomModule
             },
          },
      }
  
      Output:
      # extract all the keys in "static", "dynamic" and "weight_only" dict
      [CustomModule1, CustomModule2, CustomModule3]
      """
      # using set to dedup
      float_custom_module_classes = set()
      custom_module_mapping = custom_config_dict.get(custom_config_dict_key, {})
      for quant_mode in ["static", "dynamic", "weight_only"]:
          quant_mode_custom_module_config = custom_module_mapping.get(quant_mode, {})
          quant_mode_custom_module_classes = set(quant_mode_custom_module_config.keys())
          float_custom_module_classes |= quant_mode_custom_module_classes
      return list(float_custom_module_classes)
  
  def get_linear_prepack_op_for_dtype(dtype):
      if dtype == torch.float16:
          return torch.ops.quantized.linear_prepack_fp16
      elif dtype == torch.qint8:
          return torch.ops.quantized.linear_prepack
      else:
          raise Exception("can't get linear prepack op for dtype:", dtype)
  
  # Returns a function that can get a new attribute name for module with given
  # prefix, for example,
  # >> get_new_observer_name = get_new_attr_name_with_prefix('_observer')
  # >> new_name = get_new_observer_name(module)
  # new_name will be an unused attribute name on module, e.g. `_observer_1`
  def get_new_attr_name_with_prefix(prefix: str) -> Callable:
      def get_new_attr_name(module: torch.nn.Module):
          def get_attr_name(i: int):
              return prefix + str(i)
          i = 0
          attr_name = get_attr_name(i)
          while hasattr(module, attr_name):
              i += 1
              attr_name = get_attr_name(i)
          return attr_name
      return get_new_attr_name
  
  def collect_producer_nodes(node: Node) -> Optional[List[Node]]:
      r''' Starting from a target node, trace back until we hit inpu or
      getattr node. This is used to extract the chain of operators
      starting from getattr to the target node, for example
      def forward(self, x):
        observed = self.observer(self.weight)
        return F.linear(x, observed)
      collect_producer_nodes(observed) will either return a list of nodes that
      produces the observed node or None if we can't extract a self contained
      graph without free variables(inputs of the forward function).
      '''
      nodes = [node]
      frontier = [node]
      while frontier:
          node = frontier.pop()
          all_args = list(node.args) + list(node.kwargs.values())
          for arg in all_args:
              if not isinstance(arg, Node):
                  continue
              if arg.op == 'placeholder':
                  # hit input, can't fold in this case
                  return None
              nodes.append(arg)
              if not (arg.op == 'call_function' and arg.target == getattr):
                  frontier.append(arg)
      return nodes
  
  def graph_module_from_producer_nodes(
          root: GraphModule, producer_nodes: List[Node]) -> GraphModule:
      r''' Construct a graph module from extracted producer nodes
      from `collect_producer_nodes` function
      Args:
        root: the root module for the original graph
        producer_nodes: a list of nodes we use to construct the graph
      Return:
        A graph module constructed from the producer nodes
      '''
      assert len(producer_nodes) > 0, 'list of producer nodes can not be empty'
      # since we traced back from node to getattrr
      producer_nodes.reverse()
      graph = Graph()
      env: Dict[Any, Any] = {}
  
      def load_arg(a):
          return map_arg(a, lambda node: env[node])
      for producer_node in producer_nodes:
          env[producer_node] = graph.node_copy(producer_node, load_arg)
      graph.output(load_arg(producer_nodes[-1]))
      graph_module = GraphModule(root, graph)
      return graph_module
  
  def assert_and_get_unique_device(module: torch.nn.Module) -> Any:
      """
      Returns the unique device for a module, or None if no device is found.
      Throws an error if multiple devices are detected.
      """
      devices = {p.device for p in module.parameters()} | \
          {p.device for p in module.buffers()}
      assert len(devices) <= 1, (
          "prepare only works with cpu or single-device CUDA modules, "
          "but got devices {}".format(devices)
      )
      device = next(iter(devices)) if len(devices) > 0 else None
      return device
  