code: def test_get_norm_layer(normalization, ctx):
      class TestNet(mx.gluon.HybridBlock):
          def __init__(self):
              super().__init__()
              self.embed = mx.gluon.nn.Dense(16, in_units=16)
              self.norm_layer = get_norm_layer(
                  normalization=normalization, in_channels=16
              )
              self.pred = mx.gluon.nn.Dense(16, in_units=16)
  
          def forward(self, x):
              return self.pred(self.norm_layer(self.embed(x)))
  
      with ctx:
          net = TestNet()
          net.hybridize()
          net.initialize()
          data_in = mx.np.random.normal(0, 1, (8, 16))
          out = net(data_in)
          out_np = out.asnumpy()
  
file_path: gluon-nlp/tests/test_layers.py
imports: import pytest
  import numpy
  from numpy.testing import assert_allclose
  import mxnet
  from gluonnlp.layers import PositionalEmbedding, SinusoidalPositionalEmbedding, LearnedPositionalEmbedding, BucketPositionalEmbedding, AdaptiveEmbedding, ProjectedAdaptiveLogSoftmaxWithLoss, get_activation, get_norm_layer
  from gluonnlp.op import relative_position_bucket
label_desc: Use image node_id 7 for calling a global function with example usage: test_get_norm_layer(normalization, ctx) and returns: self