code: def _is_annotated(nodes: List[Node]):
      """
      Given a list of nodes (that represents an operator pattern),
      check if any of the node is annotated, return True if any of the node
      is annotated, otherwise return False
      """
      annotated = False
      for node in nodes:
          annotated = annotated or (
              "quantization_annotation" in node.meta
              and node.meta["quantization_annotation"]._annotated
          )
      return annotated
  
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 2 for calling a global function with example usage: _is_annotated(nodes) and returns: annotated