class_docstr: Quantized 1D adaptive average pool
class_docstr_tok: ["Quantized","1D","adaptive","average","pool"]
code: def __init__(self, output_size, **kwargs):
      super(QuantAdaptiveAvgPool1d, self).__init__(output_size)
      quant_desc_input = _utils.pop_quant_desc_in_kwargs(
          self.__class__, input_only=True, **kwargs
      )
      self.init_quantizer(quant_desc_input)
  
imports: from torch.nn.modules import pooling
  from .None import _utils
label_desc: Use image node_id 1 to create a new QuantAdaptiveAvgPool1d object from inherited base classes: pooling, _utils with example: obj = QuantAdaptiveAvgPool1d(output_size)