class_docstr: Multi-size Segmentation Evaluator
class_docstr_tok: ["Multi-size","Segmentation","Evaluator"]
code: def __init__(
      self,
      module,
      nclass,
      ctx_list,
      flip=True,
      scales=[0.5, 0.75, 1.0, 1.25, 1.5, 1.75],
  ):
      self.flip = flip
      self.ctx_list = ctx_list
      self.base_size = module.base_size
      self.crop_size = module.crop_size
      self.nclass = nclass
      self.scales = scales
      module.collect_params().reset_ctx(ctx=ctx_list)
      self.evalmodule = SegEvalModel(module)
  
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 1 to create a new MultiEvalModel object from inherited base classes: object with example: obj = MultiEvalModel(module, nclass, ctx_list, flip, scales)