class_docstr: Input node for structured data.
  
  The input data should be numpy.ndarray, pandas.DataFrame or
  tensorflow.Dataset.  The data should be two-dimensional with numerical or
  categorical values.
  
  # Arguments
      column_names: A list of strings specifying the names of the columns. The
          length of the list should be equal to the number of columns of the
          data. Defaults to None. If None, it will be obtained from the
          header of the csv file or the pandas.DataFrame.
      column_types: Dict. The keys are the column names. The values should
          either be 'numerical' or 'categorical', indicating the type of that
          column. Defaults to None. If not None, the column_names need to be
          specified. If None, it will be inferred from the data. A column will
          be judged as categorical if the number of different values is less
          than 5% of the number of instances.
      name: String. The name of the input node. If unspecified, it will be set
          automatically with the class name.
code: def get_block(self):
      return blocks.StructuredDataBlock()
  
imports: from typing import Dict
  from typing import List
  from typing import Optional
  import tensorflow
  from tensorflow import keras
  from tensorflow import nest
  from autokeras import adapters
  from autokeras import analysers
  from autokeras import blocks
  from autokeras import hyper_preprocessors
  from autokeras import keras_layers
  from autokeras import preprocessors
  from autokeras.engine import io_hypermodel
  from autokeras.engine import node
  from autokeras.utils import utils
label_desc: Use image node_id 5 for calling the StructuredDataInput obj's underlying member method code with example usage: obj.get_block() and returns: blocks