code: def forward(
      ctx,  # type: ignore[override]
      x: Tensor,
      alpha: Tensor,
      gamma: Tensor,
      quantization_levels: Tensor,
      level_indices: Tensor,
  ) -> Tensor:
      quantized_result = quantize_APoT(
          x, alpha, gamma, quantization_levels, level_indices
      )
  
      # calculate mask tensor
      mask = x.detach().apply_(lambda x: (x <= alpha and x >= -alpha))
  
      result = dequantize_APoT(quantized_result)
  
      ctx.save_for_backward(mask)
  
      return result
  
imports: import torch
  from torch import Tensor
  from torch.ao.quantization.experimental.quantizer import quantize_APoT, dequantize_APoT
label_desc: Use image node_id 1 for calling the fake_quantize_function obj's underlying member method code with example usage: obj.forward(ctx, x, alpha, gamma, quantization_levels, level_indices) and returns: result