code: def quant_levels_visualization(self, signed=False):
      (
          alpha,
          gamma,
          quantization_levels,
          level_indices,
      ) = self.calculate_qparams(signed)
  
      xs = [float(x) / 1000.0 for x in range(1000)]
      ys = [
          apot_to_float(
              float_to_apot(
                  x, quantization_levels, level_indices, alpha
              ),
              quantization_levels,
              level_indices,
          ).item()
          for x in xs
      ]
  
      f = plt.figure(figsize=(15, 10))
  
      plt.plot(xs, ys)
      plt.title("APoT Quantization Plot")
      plt.xlabel("Full Precision")
      plt.ylabel("Quantized")
      plt.show()
  
file_path: pytorch/torch/ao/quantization/experimental/observer.py
imports: import torch
  import itertools
  import matplotlib.pyplot
  from torch.ao.quantization.observer import ObserverBase
  from torch.ao.quantization.experimental.apot_utils import float_to_apot, apot_to_float
label_desc: Use image node_id 5 for calling the APoTObserver obj's underlying member method code with example usage: obj.quant_levels_visualization(signed) without return types