class_docstr: Test case for package-level functions for correctness.
class_docstr_tok: ["Test","case","for","package-level","functions","for","correctness","."]
code: def setUp(self):
      self.num_in_channels = num_in_channels = 3
      self.num_classes = num_classes = 5
      self.batch_size = 7
      self.in_h = self.in_w = 28
  
      features = nn.Sequential(
          OrderedDict(
              [
                  ("conv1", nn.Conv2d(num_in_channels, 64, 3)),
                  ("bn1", nn.BatchNorm2d(64, momentum=1, affine=True)),
                  ("relu1", nn.ReLU(inplace=True)),
                  ("pool1", nn.MaxPool2d(2, 2)),
                  ("conv2", nn.Conv2d(64, 64, 3)),
                  ("bn2", nn.BatchNorm2d(64, momentum=1, affine=True)),
                  ("relu2", nn.ReLU(inplace=True)),
                  ("pool2", nn.MaxPool2d(2, 2)),
                  ("conv3", nn.Conv2d(64, 64, 3)),
                  ("bn3", nn.BatchNorm2d(64, momentum=1, affine=True)),
                  ("relu3", nn.ReLU(inplace=True)),
                  ("pool3", nn.MaxPool2d(2, 2)),
              ]
          )
      )
      fc = nn.Linear(64, num_classes)
      self.target_net = _TargetNet(features, fc)
      self.reference_net = _ReferenceNet(
          copy.deepcopy(features), copy.deepcopy(fc)
      )
      self.lr = 0.01
      self.opt = optim.SGD(self.target_net.parameters(), lr=self.lr)
  
imports: import unittest
  import copy
  from collections import OrderedDict
  import numpy
  import torch
  from torch import nn, optim
  from torch.nn import functional
  import higher
label_desc: Use image node_id 1 for calling the TestCorrectness obj's underlying member method code with example usage: obj.setUp() without return types