class_docstr: This class implements an encoder model using the TensorFlow framework.
class_docstr_tok: ["This","class","implements","an","encoder","model","using","the","TensorFlow","framework","."]
docstr: Perform prediction for a batch of inputs.
  
  :param x: Input samples.
  :param batch_size: Batch size.
  :return: Array of encoding predictions of shape `(num_inputs, encoding_length)`.
docstr_tok: ["Perform","prediction","for","a","batch","of","inputs",".",":","param","x",":","Input","samples",".",":","param","batch_size",":","Batch","size",".",":","return",":","Array","of","encoding","predictions","of","shape","`","(","num_inputs",",","encoding_length",")","`","."]
code: def predict(self, x: "np.ndarray", batch_size: int = 128, **kwargs):
      """
      Perform prediction for a batch of inputs.
  
      :param x: Input samples.
      :param batch_size: Batch size.
      :return: Array of encoding predictions of shape `(num_inputs, encoding_length)`.
      """
      logger.info("Encoding input")
      feed_dict = {self.input_ph: x}
      if self.feed_dict is not None:
          feed_dict.update(self.feed_dict)
      y = self._sess.run(self._model, feed_dict=feed_dict)
      return y
  
file_path: adversarial-robustness-toolbox/art/estimators/encoding/tensorflow.py
imports: from __future__ import absolute_import, division, print_function, unicode_literals
  import logging
  from typing import Any, Dict, List, Optional, Union, Tuple, TYPE_CHECKING
  from art.estimators.encoding.encoder import EncoderMixin
  from art.estimators.tensorflow import TensorFlowEstimator
label_desc: Use image node_id 6 for calling the TensorFlowEncoder obj's underlying member method code with example usage: obj.predict(x, batch_size) and returns: y