docstr: Calculates the equalization scale based on the input and weight
          
docstr_tok: ["Calculates","the","equalization","scale","based","on","the","input","and","weight"]
code: def calculate_equalization_scale_ref(self, x, w):
      """Calculates the equalization scale based on the input and weight"""
      min_inputs = x.min(axis=0)
      max_inputs = x.max(axis=0)
  
      min_weights_col = w.min(axis=0)
      max_weights_col = w.max(axis=0)
  
      equalization_scale = np.sqrt(
          (max_weights_col - min_weights_col)
          / (max_inputs - min_inputs)
      )
      return equalization_scale
  
imports: import torch
  import torch.nn
  import torch.nn.functional
  import torch.ao.nn.intrinsic.quantized
  import torch.ao.nn.quantized
  from torch.ao.quantization import default_qconfig
  from torch.ao.quantization.observer import MinMaxObserver, PerChannelMinMaxObserver
  from torch.ao.quantization.quantize_fx import prepare_fx, convert_fx
  from torch.ao.quantization.fx._equalize import _InputEqualizationObserver, _WeightEqualizationObserver, calculate_equalization_scale, default_equalization_qconfig, _convert_equalization_ref, get_layer_sqnr_dict, get_equalization_qconfig_dict
  from torch.testing._internal.common_quantization import NodeSpec, QuantizationTestCase, SingleLayerLinearModel, TwoLayerLinearModel, LinearAddModel, SingleLayerFunctionalLinearModel, TwoLayerFunctionalLinearModel, FunctionalLinearAddModel, ConvModel, TwoLayerConvModel, SingleLayerFunctionalConvModel, TwoLayerFunctionalConvModel, skipIfNoFBGEMM, LinearReluModel, LinearReluLinearModel, LinearReluAddModel, FunctionalLinearReluModel, FunctionalLinearReluLinearModel, ConvReluModel, ConvReluConvModel, ConvReluAddModel, FunctionalConvReluModel, FunctionalConvReluConvModel
  import copy
  import numpy
  from hypothesis import given
  from hypothesis import strategies
label_desc: Use image node_id 6 for calling the TestEqualizeFx obj's underlying member method code with example usage: obj.calculate_equalization_scale_ref(x, w) and returns: equalization_scale