class_docstr: Class for importing MXNet Gluon models.
docstr: Fit the classifier on the training set `(inputs, outputs)`.
  
  :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 kwargs: Dictionary of framework-specific arguments. This parameter is not currently supported for MXNet
         and providing it takes no effect.
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code: def fit(
      self,
      x: np.ndarray,
      y: np.ndarray,
      batch_size: int = 128,
      nb_epochs: int = 20,
      **kwargs,
  ) -> None:  # pragma: no cover
      """
      Fit the classifier on the training set `(inputs, outputs)`.
  
      :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 kwargs: Dictionary of framework-specific arguments. This parameter is not currently supported for MXNet
             and providing it takes no effect.
      """
      import mxnet as mx
  
      if self.optimizer is None:
          raise ValueError(
              "An MXNet optimizer is required for fitting the model."
          )
  
      training_mode = True
  
      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
      )
      y_preprocessed = np.argmax(y_preprocessed, axis=1)
      nb_batch = int(np.ceil(len(x_preprocessed) / batch_size))
      ind = np.arange(len(x_preprocessed))
  
      for _ in range(nb_epochs):
          # Shuffle the examples
          np.random.shuffle(ind)
  
          # Train for one epoch
          for m in range(nb_batch):
              x_batch = mx.nd.array(
                  x_preprocessed[
                      ind[m * batch_size : (m + 1) * batch_size]
                  ].astype(config.ART_NUMPY_DTYPE)
              ).as_in_context(self.ctx)
              y_batch = mx.nd.array(
                  y_preprocessed[
                      ind[m * batch_size : (m + 1) * batch_size]
                  ]
              ).as_in_context(self.ctx)
  
              with mx.autograd.record(train_mode=training_mode):
                  # Perform prediction
                  preds = self._model(x_batch)
  
                  # Apply postprocessing
                  preds = self._apply_postprocessing(
                      preds=preds, fit=True
                  )
  
                  # Form the loss function
                  loss = self.loss(preds, y_batch)
  
              loss.backward()
  
              # Update parameters
              self.optimizer.step(batch_size)
  
imports: from __future__ import absolute_import, division, print_function, unicode_literals
  import logging
  import os
  from typing import List, Optional, Tuple, Union, TYPE_CHECKING
  import numpy
  import six
  from art import config
  from art.estimators.mxnet import MXEstimator
  from art.estimators.classification.classifier import ClassGradientsMixin, ClassifierMixin
  from art.utils import check_and_transform_label_format
label_desc: Use image node_id 6 for calling the MXClassifier obj's underlying member method code with example usage: obj.fit(x, y, batch_size, nb_epochs) without return types