class_docstr: This class implements a GAN with the TensorFlow v2 framework.
class_docstr_tok: ["This","class","implements","a","GAN","with","the","TensorFlow","v2","framework","."]
docstr: Generates a sample.
  
  :param x: A input seed.
  :param batch_size: The batch size for predictions.
  :return: The generated sample.
docstr_tok: ["Generates","a","sample",".",":","param","x",":","A","input","seed",".",":","param","batch_size",":","The","batch","size","for","predictions",".",":","return",":","The","generated","sample","."]
code: def predict(
      self, x: np.ndarray, batch_size: int = 128, **kwargs
  ) -> np.ndarray:
      """
      Generates a sample.
  
      :param x: A input seed.
      :param batch_size: The batch size for predictions.
      :return: The generated sample.
      """
      return self.generator.predict(x, batch_size=batch_size, **kwargs)
  
file_path: adversarial-robustness-toolbox/art/estimators/gan/tensorflow.py
imports: from typing import Tuple, TYPE_CHECKING, Union
  import numpy
  from art.estimators.tensorflow import TensorFlowV2Estimator
label_desc: Use image node_id 2 for calling the TensorFlowV2GAN obj's underlying member method code with example usage: obj.predict(x, batch_size) and returns: self