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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def fit(self, X, num_epochs=10, updates_epoch=None, stop_param_updates=dict(), batch_size=1, show_progressbar=False, show_epoch=False, refit=True):
 """ Fit the learner to some data. Parameters X : numpy array. The input data. num_epochs : int, optional, default 10 The number of epochs to train for. updates_epoch : int, optional, default 10 The number of updates to perform on the learning rate and neighborhood per epoch. 10 suffices for most problems. stop_param_updates : dict The epoch at which to stop updating each param. This means that the specified parameter will be reduced to 0 at the specified epoch. batch_size : int, optional, default 100 The batch size to use. Warning: batching can change your performance dramatically, depending on the task. show_progressbar : bool, optional, default False Whether to show a progressbar during training. show_epoch : bool, optional, default False Whether to print the epoch number to stdout """

        if self.data_dimensionality is None:
            self.data_dimensionality = X.shape[-1]
            self.weights = np.zeros((self.num_neurons,
                                     self.data_dimensionality))
        X = self._check_input(X)
        if not self.trained or refit:
            X = self._init_weights(X)
        else:
            if self.scaler is not None:
                self.weights = self.scaler.transform(self.weights)

        if updates_epoch is None:
            X_len = X.shape[0]
            updates_epoch = np.min([50, X_len // batch_size])

        constants = self._pre_train(stop_param_updates,
                                    num_epochs,
                                    updates_epoch)
        start = time.time()
        for epoch in tqdm(range(num_epochs), disable=not show_epoch):
            logger.info("Epoch {0} of {1}".format(epoch+1, num_epochs))

            self._epoch(X,
                        epoch,
                        batch_size,
                        updates_epoch,
                        constants,
                        show_progressbar)

        self.trained = True
        if self.scaler is not None:
            self.weights = self.scaler.inverse_transform(self.weights)
        logger.info("Total train time: {0}".format(time.time() - start))