docstr: Allows for user defined transforms to run before annotating the graph.
  This allows quantizer to allow quantizing part of the model that are otherwise not quantizable.
  For example quantizer can
  a) decompose a compound operator like scaled dot product attention,
  into bmm and softmax if quantizer knows how to quantize bmm/softmax but not sdpa
  or b) transform scalars to tensor to allow quantizing scalares.
  
  Note: this is an optional method
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code: def transform_for_annotation(
      self, model: torch.fx.GraphModule
  ) -> torch.fx.GraphModule:
      """Allows for user defined transforms to run before annotating the graph.
      This allows quantizer to allow quantizing part of the model that are otherwise not quantizable.
      For example quantizer can
      a) decompose a compound operator like scaled dot product attention,
      into bmm and softmax if quantizer knows how to quantize bmm/softmax but not sdpa
      or b) transform scalars to tensor to allow quantizing scalares.
  
      Note: this is an optional method
      """
      return model
  
imports: from abc import ABC, abstractmethod
  from dataclasses import dataclass, field
  from typing import Callable, Dict, List, Optional, Tuple, Union
  import torch
  from torch import Tensor
  from torch.ao.quantization import ObserverOrFakeQuantize
  from torch.ao.quantization.qconfig import _ObserverOrFakeQuantizeConstructor
  from torch.fx import Node
label_desc: Use image node_id 1 for calling the Quantizer obj's underlying member method code with example usage: obj.transform_for_annotation(model) and returns: model