code: def _maybe_make_input_output_share_observers(
      node: Node,
      model: torch.nn.Module,
      named_modules: Dict[str, torch.nn.Module],
  ) -> bool:
      """
      Ensures that we share an observer
      for all input arguments as well as the output argument. In detail, given
      a graph of
  
        x0 -> obs0 -> op -> x2
                    /
        x1 -> obs1 /
  
      where node obs0 points to observer instance observer0,
      obs1 points to observer1 and obs2 points to observer2, we make nodes obs1
      and ob2 point to observer0.
      Returns: whether the operation succeeded or not
      """
      first_arg = None
      # find the first non-Tensor arg
      for i in range(len(node.args)):
          if isinstance(node.args[i], (Node, list, tuple)):
              first_arg = node.args[i]
              break
  
      # if there is no non-Tensor arg, return directly
      if first_arg is None:
          return False
  
      if isinstance(first_arg, (list, tuple)):
          first_arg_arg = first_arg[0]
      elif isinstance(first_arg, Node):
          first_arg_arg = first_arg
      else:
          return False
  
      # if we have a graph such as
      #   observed_node -> non_observed_node -> cat
      # we need to navigate up to the first observer
      iteration_guard = 0
      while not _is_activation_post_process_node(
          first_arg_arg, named_modules
      ):
          if not isinstance(first_arg_arg, Node):
              return False
          # did not find an activation_post_process for the op
          if first_arg_arg.op == "placeholder":
              return False
          # trace back the args until we found the first Tensor/Node
          trace_back_node = None
          for i in range(len(first_arg_arg.args)):
              trace_back_node = first_arg_arg.args[i]
              if isinstance(trace_back_node, Node):
                  break
          if trace_back_node is None:
              return False
          first_arg_arg = trace_back_node
  
          iteration_guard += 1
          if iteration_guard > 10000:
              raise AssertionError(
                  "Unable to find observer of previous node"
              )
  
      assert isinstance(first_arg_arg, Node)
      target_to_use = first_arg_arg.target
      assert isinstance(target_to_use, str)
      obs_mod_to_use = named_modules[target_to_use]
  
      if isinstance(first_arg, (list, tuple)):
          # set all other input observer nodes to use that module
          for input_idx, input_arg in enumerate(first_arg):
              if input_idx == 0:
                  continue
              iteration_guard = 0
              while not _is_activation_post_process_node(
                  input_arg, named_modules
              ):
                  # failed to trace back since no input arg for the current node
                  if len(input_arg.args) < 1:
                      return False
                  input_arg = input_arg.args[0]
                  iteration_guard += 1
                  if iteration_guard > 10000:
                      raise AssertionError(
                          "Unable to find observer of previous node"
                      )
  
              parent_name, name = _parent_name(input_arg.target)
              setattr(named_modules[parent_name], name, obs_mod_to_use)
  
      # set the output observer node to use that module
      for output_obs_node in node.users.keys():
          assert _is_activation_post_process_node(
              output_obs_node, named_modules
          )
          parent_name, name = _parent_name(output_obs_node.target)
          setattr(named_modules[parent_name], name, obs_mod_to_use)
  
      # TODO(future PR): delete the orphaned observer modules
      return True
  
imports: import copy
  import torch
  import warnings
  from torch.fx import GraphModule
  from torch.fx.graph import Graph, Node
  from torch.fx.node import Argument
  from ..quantize import propagate_qconfig_
  from ..observer import _is_activation_post_process, _PartialWrapper
  from ..qconfig import _is_reuse_input_qconfig, QConfigAny
  from ..qconfig_mapping import QConfigMapping
  from .qconfig_mapping_utils import _generate_node_name_to_qconfig, _update_qconfig_for_fusion, _get_flattened_qconfig_dict, _update_qconfig_for_qat
  from .quantize_handler import _default_root_node_getter, _get_pattern_to_quantize_handlers, QuantizeHandler
  from torch.ao.quantization import ObserverBase, FixedQParamsObserver, FixedQParamsFakeQuantize, _DerivedObserverOrFakeQuantize
  from torch.ao.quantization.utils import Pattern, NodePattern
  from ._equalize import is_equalization_observer, node_supports_equalization
  from .pattern_utils import _sorted_patterns_dict
  from .match_utils import _MatchResultWithQConfig, _find_matches
  from .utils import _insert_dequant_stubs_for_custom_module_lstm_output, _is_custom_module_lstm, _maybe_get_custom_module_lstm_from_node_arg, _qconfig_satisfies_dtype_config_constraints, get_custom_module_class_keys, all_node_args_have_no_tensors, assert_and_get_unique_device, get_non_observable_arg_indexes_and_types, get_new_attr_name_with_prefix, node_arg_is_weight, node_arg_is_bias, NON_QUANTIZABLE_WEIGHT_OPS, ObservedGraphModuleAttrs
  from torch.ao.quantization import PlaceholderObserver
  from torch.ao.quantization.quantize import convert
  from ..utils import _parent_name, get_qconfig_dtypes, get_swapped_custom_module_class
  from ..backend_config.utils import get_pattern_to_dtype_configs, get_module_to_qat_module, get_fusion_pattern_to_root_node_getter
  from ..backend_config import BackendConfig, DTypeConfig, get_native_backend_config
  from .custom_config import PrepareCustomConfig, StandaloneModuleConfigEntry
  from torch.ao.quantization.quantizer import EdgeOrNode, QuantizationSpec, QuantizationSpecBase, FixedQParamsQuantizationSpec, SharedQuantizationSpec, DerivedQuantizationSpec
  from torch.ao.quantization import ObserverOrFakeQuantize
  from torch._subclasses import FakeTensor
  from typing import Any, Dict, List, Optional, Set, Tuple, Type, Union
  from dataclasses import asdict
label_desc: Use image node_id 27 for calling a global function with example usage: _maybe_make_input_output_share_observers(node, model, named_modules) and returns: True, False, False, False, False, False, False