class_docstr: This class contains the QConfig information for a single module.
  The list of variables / values this contains can grow depending on the
  extensibility of the qconfig mapping feature set but this currently includes:
  - if activation observer is dynamic
  - if weight observer is per channel
  
  
  Args:
      module_fqn (str): The fully qualified name (fqn) of the module that this
          information contains info relevant to qconfig for
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docstr: This returns the equalization configuration for a module.
  
  For now, it just returns the default, but as more equalization options become
  possible, this method can get more fleshed out with more nuanced granularity.
  
  
  Returns the generated equalization QConfig according to what a valid configuration is
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code: def generate_equalization_qconfig(self) -> EqualizationQConfig:
      r"""
      This returns the equalization configuration for a module.
  
      For now, it just returns the default, but as more equalization options become
      possible, this method can get more fleshed out with more nuanced granularity.
  
  
      Returns the generated equalization QConfig according to what a valid configuration is
      """
      # in this case, we just return default equalization config
      # we know this is valid because only valid modules would even
      # have this option
      return default_equalization_qconfig
  
imports: from typing import Any, Dict, Set, Tuple, Callable, List
  import torch
  import torch.nn
  import torch.ao.nn.qat
  from abc import ABC, abstractmethod
  from torch.ao.quantization.fake_quantize import FakeQuantize
  from torch.ao.quantization.fx.graph_module import GraphModule
  from torch.ao.quantization.fx._model_report.model_report_observer import ModelReportObserver
  from torch.ao.quantization.qconfig import QConfig, default_qconfig, _assert_valid_qconfig
  from torch.ao.quantization.observer import ObserverBase, default_dynamic_quant_observer, default_per_channel_weight_observer, default_observer, default_weight_observer
  from torch.ao.quantization.fx._equalize import default_equalization_qconfig, EqualizationQConfig
  from torch.ao.quantization.observer import _is_activation_post_process
label_desc: Use image node_id 3 for calling the DetectorQConfigInfo obj's underlying member method code with example usage: obj.generate_equalization_qconfig() and returns: default_equalization_qconfig