code: def _annotate_conv2d_unary(
      self,
      gm: torch.fx.GraphModule,
      quantization_config: QuantizationConfig,
  ) -> None:
      fused_partitions = []
      unary_patterns = [
          [torch.nn.Conv2d, torch.nn.ReLU],
          [torch.nn.Conv2d, torch.nn.Hardtanh],
          [torch.nn.Conv2d, torch.nn.ReLU6],
      ]
      for unary_pattern in unary_patterns:
          partitions = find_sequential_partitions(gm, unary_pattern)
          if partitions:
              # Extend the fused_partitions if partitions is not empty
              fused_partitions.extend(partitions)
  
      for fused_partition in fused_partitions:
          conv_partition, unary_partition = fused_partition
          conv_node, unary_node = self._get_output_nodes_of_partitions(
              [conv_partition, unary_partition]
          )
          if (
              conv_node.op != "call_function"
              or conv_node.target != torch.ops.aten.conv2d.default
          ):
              continue
          if _is_annotated([unary_node, conv_node]):
              continue
          self._annotate_conv_node_helper(
              conv_node, False, quantization_config
          )
          unary_node.meta[
              QUANT_ANNOTATION_KEY
          ] = _X86InductorQuantizationAnnotation(
              _annotated=True,
              _is_output_of_quantized_pattern=True,
          )
  
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 21 for calling the X86InductorQuantizer obj's underlying member method code with example usage: obj._annotate_conv2d_unary(gm, quantization_config) without return types