code: def test_batched_count_output(self):
      input_array = np.array([[1, 2, 3, 1], [0, 3, 1, 0]])
      expected_output = np.array(
          [[0, 2, 1, 1, 0, 0], [2, 1, 0, 1, 0, 0]]
      )
  
      num_tokens = 6
      expected_output_shape = (2, num_tokens)
  
      layer = layers.CategoryEncoding(num_tokens=6, output_mode="count")
      int_data = layer(input_array)
      self.assertEqual(expected_output_shape, int_data.shape)
      self.assertAllClose(int_data, expected_output)
  
      # Test symbolic call.
      output = layer(
          layers.Input(batch_shape=input_array.shape, dtype="int32")
      )
      self.assertEqual(expected_output_shape, output.shape)
      self.assertEqual("float32", output.dtype)
  
file_path: keras/keras/layers/preprocessing/category_encoding_test.py
imports: import numpy
  from tensorflow import data
  from keras import layers
  from keras import testing
label_desc: Use image node_id 2 for calling the CategoryEncodingTest obj's underlying member method code with example usage: obj.test_batched_count_output() without return types