code: def _create_and_fit_sequential_model(layer, shape):
      # Helperfunction for quick evaluation
      np.random.seed(0x2020)
      model = tf.keras.models.Sequential()
      model.add(layer)
      model.add(tf.keras.layers.Dense(32))
      model.add(tf.keras.layers.Dense(1))
  
      model.compile(
          optimizer=tf.keras.optimizers.RMSprop(0.01),
          loss="categorical_crossentropy",
      )
      layer_shape = (10,) + shape
      input_batch = np.random.rand(*layer_shape)
      output_batch = np.random.rand(*(10, 1))
      model.fit(x=input_batch, y=output_batch, epochs=1, batch_size=1)
      return model
  
imports: import pytest
  import numpy
  import tensorflow
  from tensorflow_addons.layers.normalizations import FilterResponseNormalization
  from tensorflow_addons.layers.normalizations import GroupNormalization
  from tensorflow_addons.layers.normalizations import InstanceNormalization
label_desc: Use image node_id 10 for calling a global function with example usage: _create_and_fit_sequential_model(layer, shape) and returns: model