code: def special_pattern_replacement(model: GraphModule):
      modules = dict(model.named_modules(remove_duplicate=False))
      for n in model.graph.nodes:
          q_node = n
          is_quantize = q_node.target == torch.quantize_per_tensor
          is_to_fp16 = (
              q_node.op == "call_method"
              and q_node.target == "to"
              and len(q_node.args) == 2
              and q_node.args[1] == torch.float16
          )
          if not (is_quantize or is_to_fp16):
              continue
          ref_node = q_node.args[0]
          # get output scale/zero_point/dtype from the quantize node
          # ref_node, scale_node, zero_point_node, dtype = q_node.args
          # TODO: add safety checks that users for the ref_node and dq_node needs to be one
          (
              is_call_function,
              is_call_method,
              is_call_module,
          ) = is_fixed_qparams_node(ref_node, modules)
          if is_to_fp16 and (
              is_call_function or is_call_method or is_call_module
          ):
              # TODO: add a warning or error out here? (bc-breaking if error out)
              # warnings.warn(
              #     "Only reference patterns are currently supported for {dtype} dtype with {op} op"
              #     "".format(dtype=dtypes, op=ref_node))
              continue
  
          (
              is_call_function,
              is_call_method,
              is_call_module,
          ) = is_default_node(ref_node, modules)
          if is_to_fp16 and (
              is_call_function or is_call_method or is_call_module
          ):
              # TODO: add a warning or error out here? (bc-breaking if error out)
              continue
  
          # This check includes all supported ops
          (
              is_call_function,
              is_call_method,
              is_call_module,
          ) = is_special_pattern_node(ref_node, modules)
          if not (is_call_module or is_call_function or is_call_method):
              continue
          assert len(ref_node.args) > 0 or len(ref_node.kwargs) > 0
          dq_node_or_nodes = (
              ref_node.args[0]
              if len(ref_node.args) > 0
              else next(iter(ref_node.kwargs.values()))
          )
          assert isinstance(dq_node_or_nodes, (Node, tuple, list))
          is_dequantize = False
          if isinstance(dq_node_or_nodes, Node):
              is_dequantize = (
                  dq_node_or_nodes.op == "call_method"
                  and dq_node_or_nodes.target == "dequantize"
              )
          elif isinstance(dq_node_or_nodes, (tuple, list)):
              is_dequantize = all(
                  x.op == "call_method" and x.target == "dequantize"
                  for x in dq_node_or_nodes
              )
  
          if not is_dequantize:
              continue
  
          # TODO: enable we have patterns that needs to swap the modules
          if is_call_module:
              ref_module = modules[ref_node.target]
              if (
                  type(ref_module) in SPECIAL_PATTERN_LOWER_MODULE_MAP
                  and is_quantize
              ):
                  qmodule_cls = SPECIAL_PATTERN_LOWER_MODULE_MAP.get(
                      type(ref_module)
                  )
                  scale_node = q_node.args[1]
                  zero_point_node = q_node.args[2]
                  output_scale = getattr(model, scale_node.target)
                  output_zero_point = getattr(
                      model, zero_point_node.target
                  )
  
                  qmodule = qmodule_cls.from_reference(
                      ref_module, output_scale, output_zero_point
                  )  # type:ignore[union-attr]
                  # replace reference module with quantized module
                  parent_name, module_name = _parent_name(
                      ref_node.target
                  )
                  setattr(modules[parent_name], module_name, qmodule)
  
          # reroute around dq node:
          dq_nodes: List[Node] = []
          if isinstance(dq_node_or_nodes, Node):
              dq_nodes = [dq_node_or_nodes]
          elif isinstance(dq_node_or_nodes, (tuple, list)):
              dq_nodes = list(dq_node_or_nodes)
  
          for dq_node in dq_nodes:
              dn_input = dq_node.args[0]
              ref_node.replace_input_with(dq_node, dn_input)
  
          # store q node args
          qnode_qparams = list(q_node.args)[1:]
          # replace uses of q node with input and remove q node
          q_node_input = q_node.args[0]
          q_node.replace_all_uses_with(q_node_input)
          model.graph.erase_node(q_node)
  
          (
              is_call_function,
              is_call_method,
              is_call_module,
          ) = is_default_node(ref_node, modules)
          if is_call_function:
              # pass scale/zer_point arguments from quantize_per_tensor to the default node operator
              # insert an op after the zero_point node so that the scale/zero_point
              # nodes are is available
              qop = get_quantized_operator(ref_node.target)
              args = list(ref_node.args)
              kwargs = dict(ref_node.kwargs)
              if qop in QOP_TO_ARG_NAMES_TO_SKIP:
                  args_to_skip = QOP_TO_ARG_NAMES_TO_SKIP[qop]
                  for arg in args_to_skip:
                      if arg in kwargs:
                          kwargs.pop(arg)
              kwargs["output_scale"] = qnode_qparams[0]
              kwargs["output_zero_point"] = qnode_qparams[1]
              with model.graph.inserting_after(qnode_qparams[1]):
                  qop_node = create_node_from_old_node_preserve_meta(
                      model.graph,
                      ("call_function", qop, tuple(args), kwargs),
                      ref_node,
                  )
                  ref_node.replace_all_uses_with(qop_node)
                  model.graph.erase_node(ref_node)
          else:
              # remove scale/zero_point node for quantize node
              for n in qnode_qparams:
                  if isinstance(n, Node):
                      model.graph.erase_node(n)
  
      return model
  
imports: import torch
  from torch.fx import map_arg, Node
  from torch.fx.graph import Graph
  import torch.nn
  import torch.nn.functional
  import torch.ao.nn.intrinsic
  import torch.ao.nn.intrinsic.quantized
  import torch.ao.nn.intrinsic.quantized.dynamic
  import torch.ao.nn.quantized
  import torch.ao.nn.quantized.dynamic
  import torch.ao.nn.quantized.reference
  from torch.ao.nn.quantized.modules.utils import WeightedQuantizedModule
  from torch.fx import GraphModule
  from .utils import collect_producer_nodes, get_linear_prepack_op_for_dtype, get_new_attr_name_with_prefix, get_qconv_prepack_op, graph_module_from_producer_nodes
  from ..utils import _parent_name
  from ..qconfig import QConfigAny
  from ..quantization_mappings import get_quantized_operator
  from .utils import create_node_from_old_node_preserve_meta
  from typing import Dict, Tuple, Type, List, Callable, Any, Union, Set, Optional
  import operator
label_desc: Use image node_id 25 for calling a global function with example usage: special_pattern_replacement(model) and returns: model