code: def propagate_annotation(model: torch.fx.GraphModule) -> None:
      for n in model.graph.nodes:
          if n.op != "call_function" or not _is_share_obs_or_fq_op(
              n.target
          ):
              continue
  
          prev_node = n.args[0]
          if not isinstance(prev_node, Node):
              continue
  
          quantization_annotation = prev_node.meta.get(
              "quantization_annotation", None
          )
          if not quantization_annotation:
              continue
  
          output_qspec = quantization_annotation.output_qspec
          if not output_qspec:
              continue
  
          # make sure current node is not annotated
          if (
              "quantization_annotation" in n.meta
              and n.meta["quantization_annotation"]._annotated
          ):
              continue
  
          shared_qspec = SharedQuantizationSpec(prev_node)
          # propagate the previous output_qspec to the current node
          n.meta["quantization_annotation"] = QuantizationAnnotation(
              input_qspec_map={
                  prev_node: shared_qspec,
              },
              output_qspec=shared_qspec,
              _annotated=True,
          )
  
imports: import itertools
  import operator
  from dataclasses import dataclass
  from typing import Callable, Dict, List, NamedTuple, Optional
  import torch
  import torch.nn.functional
  from torch._subclasses import FakeTensor
  from torch.ao.quantization.fx.utils import get_new_attr_name_with_prefix
  from torch.ao.quantization.pt2e.graph_utils import find_sequential_partitions
  from torch.ao.quantization.pt2e.utils import _conv1d_bn_example_inputs, _conv2d_bn_example_inputs, get_aten_graph_module
  from torch.ao.quantization.quantizer import QuantizationAnnotation, QuantizationSpec, QuantizationSpecBase, SharedQuantizationSpec
  from torch.ao.quantization.quantizer.utils import _annotate_input_qspec_map, _annotate_output_qspec
  from torch.fx import Node
  from torch.fx.passes.utils.matcher_with_name_node_map_utils import SubgraphMatcherWithNameNodeMap
  from torch.fx.passes.utils.source_matcher_utils import get_source_partitions
label_desc: Use image node_id 26 for calling a global function with example usage: propagate_annotation(model) without return types