code: def get_aten_graph_module(
      pattern: Callable,
      example_inputs: Tuple[Any, ...],
      is_cuda: bool = False,
      **kwargs,
  ) -> GraphModule:
      """
      Convert the pattern to an FX graph with decomposed aten ops.
      """
      if is_cuda:
          example_inputs = tuple(
              [
                  x.cuda() if isinstance(x, torch.Tensor) else x
                  for x in example_inputs
              ]
          )
      aten_pattern = capture_pre_autograd_graph(
          pattern,
          example_inputs,
          kwargs,
      )
      aten_pattern.graph.eliminate_dead_code()
      aten_pattern.recompile()
      return aten_pattern
  
imports: import operator
  import types
  import torch
  from torch._export import capture_pre_autograd_graph
  from torch.fx import GraphModule, Node
  from torch.nn.utils.fusion import fuse_conv_bn_weights
  from typing import Any, Callable, Dict, Optional, Tuple, List, Union
  from torch.utils._pytree import LeafSpec
  from torch.ao.quantization.fx._decomposed import quantized_decomposed_lib
  from torch.ao.quantization.quantizer import QuantizationAnnotation
label_desc: Use image node_id 15 for calling a global function with example usage: get_aten_graph_module(pattern, example_inputs, is_cuda) and returns: aten_pattern