code: def maybe_get_weight_eq_obs_node(
      op_node: Node, modules: Dict[str, nn.Module]
  ) -> Optional[Node]:
      """Gets the weight equalization observer node if it exists."""
      assert op_node.op == "call_function"
      for node_arg in op_node.args:
          if node_arg_is_weight(op_node, node_arg):
              assert (
                  isinstance(node_arg, Node)
                  and node_arg.op == "call_module"
                  and isinstance(
                      modules[str(node_arg.target)],
                      _WeightEqualizationObserver,
                  )
              )
              return node_arg
      return None
  
imports: import warnings
  from collections import namedtuple
  from typing import Any, Dict, List, Optional, Tuple
  import torch
  import torch.nn
  import torch.nn.functional
  import torch.ao.nn.intrinsic
  from torch.fx import GraphModule
  from torch.fx.graph import Node
  from torch.ao.quantization.fx.graph_module import _get_observed_graph_module_attr
  from ..observer import _with_args, ObserverBase, PerChannelMinMaxObserver
  from ..utils import _parent_name, check_min_max_valid
  from .utils import get_new_attr_name_with_prefix, maybe_get_next_module, node_arg_is_weight
label_desc: Use image node_id 9 for calling a global function with example usage: maybe_get_weight_eq_obs_node(op_node, modules) and returns: None, node_arg