code: def _to_tensor(self):
      x = self.model_inputs[0].zvalue
      y = zlayers.GlobalAveragePooling2D()(x)
      """
          Input data tensor from the previous operator; dimensions for image case are (N x C x H x W),
          where N is the batch size, C is the number of channels, and H and W are the height
          and the width of the data.
          Output data tensor from pooling across the input tensor. Dimensions will be N x C x 1 x 1.
          """
      return zautograd.expand_dims(
          zautograd.expand_dims(y, axis=2), axis=3
      )
  
file_path: analytics-zoo/pyzoo/zoo/pipeline/api/onnx/mapper/globalaveragepool.py
imports: from zoo.pipeline.api.onnx.mapper.operator_mapper import OperatorMapper
  from zoo.pipeline.api.onnx.onnx_helper import OnnxHelper
  import zoo.pipeline.api.keras.layers
  import zoo.pipeline.api.autograd
  import numpy
label_desc: Use image node_id 2 for calling the GlobalAveragePoolMapper obj's underlying member method code with example usage: obj._to_tensor() and returns: zautograd