code: def _annotate_output_share_observer_as_input(
      self, input_node: Node, source_node: Node
  ):
      source_node_quantization_annotation = (
          source_node.meta[QUANT_ANNOTATION_KEY]
          if QUANT_ANNOTATION_KEY in source_node.meta
          else None
      )
      if (
          source_node_quantization_annotation
          and source_node_quantization_annotation._is_output_of_quantized_pattern
      ):
          edge_or_node = (input_node, source_node)
          source_node_quantization_annotation.output_qspec = (
              SharedQuantizationSpec(edge_or_node)
          )
      return
  
imports: import copy
  import functools
  import itertools
  import operator
  from dataclasses import dataclass
  from typing import Any, Dict, List, Optional, Sequence, Set, Tuple
  import torch
  import torch.nn.functional
  from torch.ao.quantization.fake_quantize import FakeQuantize, FusedMovingAvgObsFakeQuantize
  from torch.ao.quantization.observer import HistogramObserver, MovingAverageMinMaxObserver, MovingAveragePerChannelMinMaxObserver, PerChannelMinMaxObserver, PlaceholderObserver
  from torch.ao.quantization.pt2e.graph_utils import find_sequential_partitions
  from torch.ao.quantization.qconfig import _ObserverOrFakeQuantizeConstructor
  from torch.ao.quantization.quantizer.quantizer import QuantizationAnnotation, QuantizationSpec, Quantizer, SharedQuantizationSpec
  from torch.ao.quantization.quantizer.xnnpack_quantizer_utils import _is_annotated, get_bias_qspec, get_input_act_qspec, get_output_act_qspec, get_weight_qspec, OperatorConfig, OperatorPatternType, QuantizationConfig
  from torch.fx import Node
  from torch.fx.passes.utils.source_matcher_utils import get_source_partitions, SourcePartition
label_desc: Use image node_id 26 for calling the X86InductorQuantizer obj's underlying member method code with example usage: obj._annotate_output_share_observer_as_input(input_node, source_node) without return types