class_docstr: This class implements a classifier with the TensorFlow framework.
class_docstr_tok: ["This","class","implements","a","classifier","with","the","TensorFlow","framework","."]
docstr: Fit the classifier on the training set `(x, y)`.
  
  :param x: Training data.
  :param y: Target values (class labels) one-hot-encoded of shape (nb_samples, nb_classes) or index labels of
            shape (nb_samples,).
  :param batch_size: Size of batches.
  :param nb_epochs: Number of epochs to use for training.
  :param verbose: Display the training progress bar.
  :param kwargs: Dictionary of framework-specific arguments. This parameter is not currently supported for
         TensorFlow and providing it takes no effect.
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code: def fit(  # pylint: disable=W0221
      self,
      x: np.ndarray,
      y: np.ndarray,
      batch_size: int = 128,
      nb_epochs: int = 10,
      verbose: bool = False,
      **kwargs,
  ) -> None:
      """
      Fit the classifier on the training set `(x, y)`.
  
      :param x: Training data.
      :param y: Target values (class labels) one-hot-encoded of shape (nb_samples, nb_classes) or index labels of
                shape (nb_samples,).
      :param batch_size: Size of batches.
      :param nb_epochs: Number of epochs to use for training.
      :param verbose: Display the training progress bar.
      :param kwargs: Dictionary of framework-specific arguments. This parameter is not currently supported for
             TensorFlow and providing it takes no effect.
      """
      if self.learning is not None:
          self.feed_dict[self.learning] = True
  
      # Check if train and output_ph available
      if (
          self.train is None or self.labels_ph is None
      ):  # pragma: no cover
          raise ValueError(
              "Need the training objective and the output placeholder to train the model."
          )
  
      y = check_and_transform_label_format(
          y, nb_classes=self.nb_classes
      )
  
      # Apply preprocessing
      x_preprocessed, y_preprocessed = self._apply_preprocessing(
          x, y, fit=True
      )
  
      # Check label shape
      if self._reduce_labels:
          y_preprocessed = np.argmax(y_preprocessed, axis=1)
  
      num_batch = int(np.ceil(len(x_preprocessed) / float(batch_size)))
      ind = np.arange(len(x_preprocessed)).tolist()
  
      # Start training
      for _ in tqdm(
          range(nb_epochs), disable=not verbose, desc="Epochs"
      ):
          # Shuffle the examples
          random.shuffle(ind)
  
          # Train for one epoch
          for m in range(num_batch):
              i_batch = x_preprocessed[
                  ind[m * batch_size : (m + 1) * batch_size]
              ]
              o_batch = y_preprocessed[
                  ind[m * batch_size : (m + 1) * batch_size]
              ]
  
              # Create feed_dict
              feed_dict = {
                  self.input_ph: i_batch,
                  self.labels_ph: o_batch,
              }
              feed_dict.update(self.feed_dict)
  
              # Run train step
              self._sess.run(self.train, feed_dict=feed_dict)
  
imports: from __future__ import absolute_import, division, print_function, unicode_literals
  import logging
  import os
  import random
  import shutil
  import time
  from typing import Any, Callable, Dict, List, Optional, Tuple, Union, TYPE_CHECKING
  from tqdm.auto import tqdm
  import numpy
  import six
  from art import config
  from art.estimators.classification.classifier import ClassGradientsMixin, ClassifierMixin
  from art.estimators.tensorflow import TensorFlowEstimator, TensorFlowV2Estimator
  from art.utils import check_and_transform_label_format
label_desc: Use image node_id 11 for calling the TensorFlowClassifier obj's underlying member method code with example usage: obj.fit(x, y, batch_size, nb_epochs, verbose) without return types