class_docstr: AutoKeras image object detector 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: Predict the output for a given testing data.
  
  # Arguments
      x: numpy.ndarray or tensorflow.Dataset. Testing data x. The shape of
          the data should be (samples, width, height) or (samples, width,
          height, channels).
      **kwargs: Any arguments supported by keras.Model.predict.
  
  # Returns
      labels: [batch_size, 3] shaped tensor containing tuples of (bboxes,
          classIDs, scores) for each image in the testing data x, where
          each bounding box is defined by 4 values [ymin, xmin, ymax,
          xmax]. Box coordinates are measured from top left image corner,
          are 0-indexed and proportional to sides i.e. between [0,1].
          Shape of the bounding boxes should be (None, 4), and shape of
          the classIDs should be (None,) in each tuple, where None
          represents the number of bounding boxes detected in an image.
          The scores denote the probability with which a class is detected
          in the corresponding bounding box.
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code: def predict(self, x, **kwargs):
      """Predict the output for a given testing data.
  
      # Arguments
          x: numpy.ndarray or tensorflow.Dataset. Testing data x. The shape of
              the data should be (samples, width, height) or (samples, width,
              height, channels).
          **kwargs: Any arguments supported by keras.Model.predict.
  
      # Returns
          labels: [batch_size, 3] shaped tensor containing tuples of (bboxes,
              classIDs, scores) for each image in the testing data x, where
              each bounding box is defined by 4 values [ymin, xmin, ymax,
              xmax]. Box coordinates are measured from top left image corner,
              are 0-indexed and proportional to sides i.e. between [0,1].
              Shape of the bounding boxes should be (None, 4), and shape of
              the classIDs should be (None,) in each tuple, where None
              represents the number of bounding boxes detected in an image.
              The scores denote the probability with which a class is detected
              in the corresponding bounding box.
      """
      pass
  
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 3 for calling the ImageObjectDetector obj's underlying member method code with example usage: obj.predict(x) without return types