class_docstr: Encode the categorical features to numerical features.
class_docstr_tok: ["Encode","the","categorical","features","to","numerical","features","."]
code: def build(self, hp, inputs=None):
      input_node = nest.flatten(inputs)[0]
      encoding = []
      for column_name in self.column_names:
          column_type = self.column_types[column_name]
          if column_type == analysers.CATEGORICAL:
              # TODO: Search to use one-hot or int.
              encoding.append(keras_layers.INT)
          else:
              encoding.append(keras_layers.NONE)
      return keras_layers.MultiCategoryEncoding(encoding)(input_node)
  
imports: from typing import Optional
  from typing import Tuple
  from typing import Union
  from keras_tuner.engine import hyperparameters
  from tensorflow import nest
  from tensorflow.keras import layers
  from autokeras import analysers
  from autokeras import keras_layers
  from autokeras.engine import block
  from autokeras.utils import io_utils
  from autokeras.utils import utils
label_desc: Use image node_id 2 for calling the CategoricalToNumerical obj's underlying member method code with example usage: obj.build(hp, inputs) and returns: keras_layers