class_docstr: AutoKeras structured data classification class.
  
  # 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 excluding the target column. Defaults to None. If None, it will
          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.
      num_classes: Int. Defaults to None. If None, it will be inferred from
          the data.
      multi_label: Boolean. Defaults to False.
      loss: A Keras loss function. Defaults to use 'binary_crossentropy' or
          'categorical_crossentropy' based on the number of classes.
      metrics: A list of Keras metrics. Defaults to use 'accuracy'.
      project_name: String. The name of the AutoModel. Defaults to
          'structured_data_classifier'.
      max_trials: Int. The maximum number of different Keras Models to try.
          The search may finish before reaching the max_trials. Defaults to
          100.
      directory: String. The path to a directory for storing the search
          outputs. Defaults to None, which would create a folder with the
          name of the AutoModel in the current directory.
      objective: String. Name of model metric to minimize
          or maximize. Defaults to 'val_accuracy'.
      tuner: String or subclass of AutoTuner. If string, it should be one of
          'greedy', 'bayesian', 'hyperband' or 'random'. It can also be a
          subclass of AutoTuner. If left unspecified, it uses a task specific
          tuner, which first evaluates the most commonly used models for the
          task before exploring other models.
      overwrite: Boolean. Defaults to `False`. If `False`, reloads an existing
          project of the same name if one is found. Otherwise, overwrites the
          project.
      seed: Int. Random seed.
      max_model_size: Int. Maximum number of scalars in the parameters of a
          model. Models larger than this are rejected.
      **kwargs: Any arguments supported by AutoModel.
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docstr: Search for the best model and hyperparameters for the AutoModel.
  
  # Arguments
      x: String, numpy.ndarray, pandas.DataFrame or tensorflow.Dataset.
          Training data x. If the data is from a csv file, it should be a
          string specifying the path of the csv file of the training data.
      y: String, numpy.ndarray, or tensorflow.Dataset. Training data y.
          If the data is from a csv file, it should be a string, which is
          the name of the target column. Otherwise, It can be raw labels,
          one-hot encoded if more than two classes, or binary encoded for
          binary classification.
      epochs: Int. The number of epochs to train each model during the
          search. If unspecified, we would use epochs equal to 1000 and
          early stopping with patience equal to 10.
      callbacks: List of Keras callbacks to apply during training and
          validation.
      validation_split: Float between 0 and 1. Defaults to 0.2. Fraction
          of the training data to be used as validation data.  The model
          will set apart this fraction of the training data, will not
          train on it, and will evaluate the loss and any model metrics on
          this data at the end of each epoch.  The validation data is
          selected from the last samples in the `x` and `y` data provided,
          before shuffling. This argument is not supported when `x` is a
          dataset.
      validation_data: Data on which to evaluate the loss and any model
          metrics at the end of each epoch. The model will not be trained
          on this data.  `validation_data` will override
          `validation_split`. The type of the validation data should be
          the same as the training data.
      **kwargs: Any arguments supported by
          [keras.Model.fit](https://www.tensorflow.org/api_docs/python/tf/keras/Model#fit).
  # Returns
      history: A Keras History object corresponding to the best model.
          Its History.history attribute is a record of training
          loss values and metrics values at successive epochs, as well as
          validation loss values and validation metrics values (if
          applicable).
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code: def fit(
      self,
      x=None,
      y=None,
      epochs=None,
      callbacks=None,
      validation_split=0.2,
      validation_data=None,
      **kwargs,
  ):
      """Search for the best model and hyperparameters for the AutoModel.
  
      # Arguments
          x: String, numpy.ndarray, pandas.DataFrame or tensorflow.Dataset.
              Training data x. If the data is from a csv file, it should be a
              string specifying the path of the csv file of the training data.
          y: String, numpy.ndarray, or tensorflow.Dataset. Training data y.
              If the data is from a csv file, it should be a string, which is
              the name of the target column. Otherwise, It can be raw labels,
              one-hot encoded if more than two classes, or binary encoded for
              binary classification.
          epochs: Int. The number of epochs to train each model during the
              search. If unspecified, we would use epochs equal to 1000 and
              early stopping with patience equal to 10.
          callbacks: List of Keras callbacks to apply during training and
              validation.
          validation_split: Float between 0 and 1. Defaults to 0.2. Fraction
              of the training data to be used as validation data.  The model
              will set apart this fraction of the training data, will not
              train on it, and will evaluate the loss and any model metrics on
              this data at the end of each epoch.  The validation data is
              selected from the last samples in the `x` and `y` data provided,
              before shuffling. This argument is not supported when `x` is a
              dataset.
          validation_data: Data on which to evaluate the loss and any model
              metrics at the end of each epoch. The model will not be trained
              on this data.  `validation_data` will override
              `validation_split`. The type of the validation data should be
              the same as the training data.
          **kwargs: Any arguments supported by
              [keras.Model.fit](https://www.tensorflow.org/api_docs/python/tf/keras/Model#fit).
      # Returns
          history: A Keras History object corresponding to the best model.
              Its History.history attribute is a record of training
              loss values and metrics values at successive epochs, as well as
              validation loss values and validation metrics values (if
              applicable).
      """
      history = super().fit(
          x=x,
          y=y,
          epochs=epochs,
          callbacks=callbacks,
          validation_split=validation_split,
          validation_data=validation_data,
          **kwargs,
      )
      return history
  
imports: import pathlib
  from typing import Dict
  from typing import List
  from typing import Optional
  from typing import Type
  from typing import Union
  import pandas
  import tensorflow
  from tensorflow import nest
  from autokeras import auto_model
  from autokeras import blocks
  from autokeras import nodes
  from autokeras.engine import tuner
  from autokeras.tuners import task_specific
  from autokeras.utils import types
label_desc: Use image node_id 2 for calling the StructuredDataClassifier obj's underlying member method code with example usage: obj.fit(x, y, epochs, callbacks, validation_split, validation_data) and returns: history