code: def _annotate_cat(
      self, node: Node, quantization_config: QuantizationConfig
  ) -> None:
      cat_node = node
      input_nodes = cat_node.args[0]
      assert isinstance(input_nodes, Sequence)
      first_input_node = input_nodes[0]
      input_qspec_map = {}
      assert isinstance(first_input_node, Node)
      assert isinstance(cat_node, Node)
      input_qspec_map[first_input_node] = get_input_act_qspec(
          quantization_config
      )
      share_qparams_with_input_act0_qspec = SharedQuantizationSpec(
          (first_input_node, cat_node)
      )
  
      for input_node in input_nodes[1:]:
          if input_node not in input_qspec_map:
              # There has the case of cat same nodes: torch.cat([input0, input0], 1)
              assert isinstance(input_node, Node)
              input_qspec_map[
                  input_node
              ] = share_qparams_with_input_act0_qspec
  
      cat_node.meta[
          QUANT_ANNOTATION_KEY
      ] = _X86InductorQuantizationAnnotation(
          input_qspec_map=input_qspec_map,
          _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 24 for calling the X86InductorQuantizer obj's underlying member method code with example usage: obj._annotate_cat(node, quantization_config) without return types