code: def _is_quantize(n: Node) -> bool:
      return n.target in [
          torch.ops.quantized_decomposed.quantize_per_tensor.default,
          torch.ops.quantized_decomposed.quantize_per_tensor.tensor,
          torch.ops.quantized_decomposed.quantize_per_channel.default,
      ]
  
imports: import dataclasses
  import itertools
  import operator
  from typing import Any, Callable, Dict, List, Tuple, TYPE_CHECKING
  import torch
  from torch.fx import Graph, GraphModule, Node
  from torch.fx.subgraph_rewriter import replace_pattern_with_filters, ReplacedPatterns
  import torch.nn.functional
  from torch.ao.quantization.fx._decomposed import quantized_decomposed_lib
  from torch.ao.quantization.quantizer import DerivedQuantizationSpec, EdgeOrNode, SharedQuantizationSpec, QuantizationSpecBase
  from .utils import _conv1d_bn_example_inputs, _conv2d_bn_example_inputs, _is_conv, _is_bn_node, fold_bn_weights_into_conv_node, get_aten_graph_module
label_desc: Use image node_id 10 for calling a global function with example usage: _is_quantize(n) and returns: unknown