code: def _run(  # type: ignore
      self,
      x,
      auto_pad=None,
      ceil_mode=None,
      dilations=None,
      kernel_shape=None,
      p=2,
      pads=None,
      strides=None,
      count_include_pad=None,
  ):
      # utilize AvgPool the same fashion Pytorch does. Note that there is a difference in computation.
      # it needs another PR to address.
      # https://github.com/pytorch/pytorch/blob/f58ba553b78db7f88477f9ba8c9333bd1590e30a/torch/nn/functional.py#L1015
      power_average = CommonPool._run(
          self,
          "AVG",
          count_include_pad,
          np.power(np.absolute(x), p),
          auto_pad=auto_pad,
          ceil_mode=ceil_mode,
          dilations=dilations,
          kernel_shape=kernel_shape,
          pads=pads,
          strides=strides,
      )
  
      kernel_element_count = np.prod(kernel_shape)
      return (
          np.power(kernel_element_count * power_average[0], 1.0 / p),
      )
  
imports: import numpy
  from onnx.reference.ops.op_pool_common import CommonPool
label_desc: Use image node_id 1 for calling the LpPool obj's underlying member method code with example usage: obj._run(x, auto_pad, ceil_mode, dilations, kernel_shape, p, pads, strides, count_include_pad) and returns: np