class_docstr: Multi-size Segmentation Evaluator
class_docstr_tok: ["Multi-size","Segmentation","Evaluator"]
code: def parallel_forward(self, inputs):
      inputs = tuple(
          [
              tuple([x.as_in_context(ctx)])
              for (x, ctx) in zip(inputs, self.ctx_list)
          ]
      )
      if len(self.ctx_list) == 1:
          return tuple_map(self(*inputs[0]))
      return parallel_apply(self, inputs, sync=True)
  
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 2 for calling the MultiEvalModel obj's underlying member method code with example usage: obj.parallel_forward(inputs) and returns: parallel_apply, tuple_map