class_docstr: Block for structured data.
  
  # Arguments
      categorical_encoding: Boolean. Whether to use the CategoricalToNumerical
          to encode the categorical features to numerical features. Defaults
          to True.
      normalize: Boolean. Whether to normalize the features.
          If unspecified, it will be tuned automatically.
      seed: Int. Random seed.
code: def __init__(
      self,
      categorical_encoding: bool = True,
      normalize: Optional[bool] = None,
      seed: Optional[int] = None,
      **kwargs,
  ):
      super().__init__(**kwargs)
      self.categorical_encoding = categorical_encoding
      self.normalize = normalize
      self.seed = seed
      self.column_types = None
      self.column_names = None
  
imports: from typing import Optional
  from tensorflow import nest
  from autokeras.blocks import basic
  from autokeras.blocks import preprocessing
  from autokeras.blocks import reduction
  from autokeras.engine import block
label_desc: Use image node_id 1 to create a new StructuredDataBlock object from inherited base classes: block_module with example: obj = StructuredDataBlock(categorical_encoding, normalize, seed)