class_docstr: Multi-size Segmentation Evaluator
class_docstr_tok: ["Multi-size","Segmentation","Evaluator"]
code: def flip_inference(self, image):
      assert isinstance(image, NDArray)
      output = self.evalmodule(image)
      if self.flip:
          fimg = _flip_image(image)
          foutput = self.evalmodule(fimg)
          output += _flip_image(foutput)
      return output.exp()
  
imports: import math
  import warnings
  import numpy
  import mxnet
  from mxnet.ndarray import NDArray
  from mxnet.gluon.nn import HybridBlock
  from ..utils.parallel import parallel_apply
  from .resnetv1b import resnet18_v1b, resnet34_v1b, resnet50_v1s, resnet101_v1s, resnet152_v1s
  from .resnest import resnest50, resnest101, resnest200, resnest269
  from ..utils.parallel import tuple_map
label_desc: Use image node_id 4 for calling the MultiEvalModel obj's underlying member method code with example usage: obj.flip_inference(image) and returns: output