code: def _is_supported_batch_norm_for_training(node: Node):
      """
      Return True if the given node refers to an aten batch norm op QAT supports.
      """
      supported_ops = [
          torch.ops.aten._native_batch_norm_legit.default,
          # Note: we won't need this op anymore after batch norm consolidation
          # For now, we need to continue to support it because it gives better
          # training numerics than `_native_batch_norm_legit`
          torch.ops.aten.cudnn_batch_norm.default,
          torch.ops.aten.miopen_batch_norm.default,
      ]
      return node.target in supported_ops
  
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 8 for calling a global function with example usage: _is_supported_batch_norm_for_training(node) and returns: unknown