code: def test_batch_norm_is_match(self):
      m = nn.BatchNorm2d(num_features=self.c)
      f = ft.partial(
          F.batch_norm,
          running_mean=m.running_mean,
          running_var=m.running_var,
          weight=m.weight,
          bias=m.bias,
          training=True,
      )
      run_layer_test(
          self, [m], MATCH_INPUT, (self.b, self.c, self.h, self.h)
      )
  
      # Test forward-only for BN inference
      m.eval()
      f = ft.partial(
          F.batch_norm,
          running_mean=m.running_mean,
          running_var=m.running_var,
          weight=m.weight,
          bias=m.bias,
          training=False,
      )
      run_layer_test(
          self,
          [m, f],
          MATCH_INPUT,
          (self.b, self.c, self.h, self.h),
          test_backward=False,
      )
  
imports: import unittest
  import functools
  import itertools
  from apex import amp
  import torch
  from torch import nn
  import torch.nn.functional
  from utils import common_init, HALF, FLOAT, ALWAYS_HALF, ALWAYS_FLOAT, MATCH_INPUT
label_desc: Use image node_id 9 for calling the TestBasicCasts obj's underlying member method code with example usage: obj.test_batch_norm_is_match() without return types