class_docstr: Mixin abstract base class defining additional functionality required for neural network estimators. This base class
  has to be mixed in with class `BaseEstimator`.
class_docstr_tok: ["Mixin","abstract","base","class","defining","additional","functionality","required","for","neural","network","estimators",".","This","base","class","has","to","be","mixed","in","with","class","`","BaseEstimator","`","."]
docstr: Fit the model of the estimator on the training data `x` and `y`.
  
  :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 y: Target values.
  :type y: Format as expected by the `model`
  :param batch_size: Batch size.
  :param nb_epochs: Number of training epochs.
code: def fit(
      self,
      x: np.ndarray,
      y,
      batch_size: int = 128,
      nb_epochs: int = 20,
      **kwargs,
  ) -> None:
      """
      Fit the model of the estimator on the training data `x` and `y`.
  
      :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 y: Target values.
      :type y: Format as expected by the `model`
      :param batch_size: Batch size.
      :param nb_epochs: Number of training epochs.
      """
      raise NotImplementedError
  
imports: from abc import ABC, abstractmethod
  from typing import Any, Dict, List, Optional, Tuple, Union, TYPE_CHECKING
  import numpy
  from tqdm.auto import trange
  from art.config import ART_NUMPY_DTYPE
label_desc: Use image node_id 3 for calling the NeuralNetworkMixin obj's underlying member method code with example usage: obj.fit(x, y, batch_size, nb_epochs) without return types