class_docstr: Estimator class for TensorFlow v2 models.
class_docstr_tok: ["Estimator","class","for","TensorFlow","v2","models","."]
docstr: Perform prediction of the neural network for samples `x`.
  
  :param x: Samples of shape (nb_samples, nb_features) or (nb_samples, nb_pixels_1, nb_pixels_2,
            nb_channels) or (nb_samples, nb_channels, nb_pixels_1, nb_pixels_2).
  :param batch_size: Batch size.
  :return: Predictions.
  :rtype: Format as expected by the `model`
docstr_tok: ["Perform","prediction","of","the","neural","network","for","samples","`","x","`",".",":","param","x",":","Samples","of","shape","(","nb_samples",",","nb_features",")","or","(","nb_samples",",","nb_pixels_1",",","nb_pixels_2",",","nb_channels",")","or","(","nb_samples",",","nb_channels",",","nb_pixels_1",",","nb_pixels_2",")",".",":","param","batch_size",":","Batch","size",".",":","return",":","Predictions",".",":","rtype",":","Format","as","expected","by","the","`","model","`"]
code: def predict(self, x: np.ndarray, batch_size: int = 128, **kwargs):
      """
      Perform prediction of the neural network for samples `x`.
  
      :param x: Samples of shape (nb_samples, nb_features) or (nb_samples, nb_pixels_1, nb_pixels_2,
                nb_channels) or (nb_samples, nb_channels, nb_pixels_1, nb_pixels_2).
      :param batch_size: Batch size.
      :return: Predictions.
      :rtype: Format as expected by the `model`
      """
      return NeuralNetworkMixin.predict(
          self, x, batch_size=batch_size, **kwargs
      )
  
imports: import logging
  from typing import Any, Tuple, TYPE_CHECKING
  import numpy
  from art import config
  from art.estimators.estimator import BaseEstimator, LossGradientsMixin, NeuralNetworkMixin
label_desc: Use image node_id 2 for calling the TensorFlowV2Estimator obj's underlying member method code with example usage: obj.predict(x, batch_size) and returns: NeuralNetworkMixin