code: def testKerasInteropSequential(self):
      class ProjectionLayer(tf.keras.layers.Layer):
          """Linear projection layer using TF NumPy."""
  
          def __init__(self, units):
              super(ProjectionLayer, self).__init__()
              self._units = units
  
          def build(self, input_shape):
              stddev = np.sqrt(self._units).astype(np.float32)
              initial_value = (
                  np.random.randn(input_shape[1], self._units).astype(
                      np.float32
                  )
                  / stddev
              )
              # Note that TF NumPy can interoperate with tf.Variable.
              self.w = tf.Variable(initial_value, trainable=True)
  
          def call(self, inputs):
              return np.matmul(inputs, self.w)
  
      model = tf.keras.Sequential(
          [tf.keras.layers.Dense(100), ProjectionLayer(2)]
      )
      output = model.call(np.random.randn(10, 100).astype(np.float32))
  
      self.assertIsInstance(output, np.ndarray)
  
      dense_layer = tf.keras.layers.Dense(100)
      output = dense_layer(np.random.randn(10, 100).astype(np.float32))
  
imports: import numpy
  import tensorflow.compat.v2
  from tensorflow.python.framework import ops
  from tensorflow.python.framework import test_util
  from tensorflow.python.ops.numpy_ops import np_math_ops
label_desc: Use image node_id 15 for calling the InteropTest obj's underlying member method code with example usage: obj.testKerasInteropSequential() and returns: np