class_docstr: AutoKeras image classification class.
  
  # Arguments
      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 'image_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, e.g. 'val_accuracy'. Defaults to 'val_loss'.
      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.
  
  It will search for the best model based on the performances on
  validation data.
  
  # Arguments
      x: numpy.ndarray or tensorflow.Dataset. Training data x. The shape
          of the data should be (samples, width, height) or (samples,
          width, height, channels).
      y: numpy.ndarray or tensorflow.Dataset. Training data y. 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, by default we train for a maximum of
          1000 epochs, but we stop training if the validation loss stops
          improving for 10 epochs (unless you specified an EarlyStopping
          callback as part of the callbacks argument, in which case the
          EarlyStopping callback you specified will determine early
          stopping).
      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. The best model found would be fit on the entire dataset
          including the validation data.
      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. The best model found would be
          fit on the training dataset without the validation 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: Optional[types.DatasetType] = None,
      y: Optional[types.DatasetType] = None,
      epochs: Optional[int] = None,
      callbacks: Optional[List[keras.callbacks.Callback]] = None,
      validation_split: Optional[float] = 0.2,
      validation_data: Union[
          tf.data.Dataset,
          Tuple[types.DatasetType, types.DatasetType],
          None,
      ] = None,
      **kwargs,
  ):
      """Search for the best model and hyperparameters for the AutoModel.
  
      It will search for the best model based on the performances on
      validation data.
  
      # Arguments
          x: numpy.ndarray or tensorflow.Dataset. Training data x. The shape
              of the data should be (samples, width, height) or (samples,
              width, height, channels).
          y: numpy.ndarray or tensorflow.Dataset. Training data y. 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, by default we train for a maximum of
              1000 epochs, but we stop training if the validation loss stops
              improving for 10 epochs (unless you specified an EarlyStopping
              callback as part of the callbacks argument, in which case the
              EarlyStopping callback you specified will determine early
              stopping).
          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. The best model found would be fit on the entire dataset
              including the validation data.
          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. The best model found would be
              fit on the training dataset without the validation 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: from pathlib import Path
  from typing import List
  from typing import Optional
  from typing import Tuple
  from typing import Type
  from typing import Union
  import tensorflow
  from tensorflow import keras
  from autokeras import auto_model
  from autokeras import blocks
  from autokeras import nodes
  from autokeras.engine import tuner
  from autokeras.tuners import greedy
  from autokeras.tuners import task_specific
  from autokeras.utils import types
label_desc: Use image node_id 2 for calling the ImageClassifier obj's underlying member method code with example usage: obj.fit(x, y, epochs, callbacks, validation_split, validation_data) and returns: history