class_docstr: Test case for package-level functions for correctness.
class_docstr_tok: ["Test","case","for","package-level","functions","for","correctness","."]
docstr: Verify fast weight alignment/equality after monkey patching.
docstr_tok: ["Verify","fast","weight","alignment\/equality","after","monkey","patching","."]
code: def testSameInitialWeightsPostPatch(self):
      """Verify fast weight alignment/equality after monkey patching."""
      ref_named_params = list(
          self.reference_net.get_fast_weights().items()
      )
      ref_params = [p for (_, p) in ref_named_params]
      with higher.innerloop_ctx(self.target_net, self.opt) as (fnet, _):
          target_named_params = list(fnet.named_parameters())
          target_params = fnet.parameters()
          self.assertEqual(
              len(ref_named_params),
              len(target_named_params),
              msg=(
                  "Length mismatched between reference net parameter count "
                  "({}) and target ({}).".format(
                      len(ref_named_params), len(target_named_params)
                  )
              ),
          )
          for ref, target in zip(ref_named_params, target_named_params):
              ref_name, ref_p = ref
              target_name, target_p = target
              self.assertEqual(
                  ref_name,
                  target_name,
                  msg="Name mismatch or parameter misalignment ('{}' vs '{}')".format(
                      ref_name, target_name
                  ),
              )
              self.assertTrue(
                  torch.equal(ref_p, target_p),
                  msg="Parameter value inequality for {}".format(
                      ref_name
                  ),
              )
          zipped = zip(ref_params, target_params)
          for i, (ref_p, target_p) in enumerate(zipped):
              self.assertTrue(
                  torch.equal(ref_p, target_p),
                  msg="Parameter misalignment in position {}.".format(
                      i
                  ),
              )
  
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 3 for calling the TestCorrectness obj's underlying member method code with example usage: obj.testSameInitialWeightsPostPatch() without return types