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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, augment=False, rounds=1, seed=None):
 """Fits internal statistics to some sample data. Required for featurewise_center, featurewise_std_normalization and zca_whitening. # Arguments x: Numpy array, the data to fit on. Should have rank 5 or 4 when time_delay is None. In case of grayscale data, the channels axis should have value 1, and in case of RGB data, it should have value 3. augment: Whether to fit on randomly augmented samples rounds: If `augment`, how many augmentation passes to do over the data seed: random seed. # Raises ValueError: in case of invalid input `x`. """

        x = np.asarray(x, dtype=K.floatx())
        if x.shape[self.channel_axis] not in {1, 3, 4}:
            warnings.warn(
                'Expected input to be images (as Numpy array) '
                'following the data format convention "' + self.data_format + '" '
                                                                              '(channels on axis ' + str(
                    self.channel_axis) + '), i.e. expected '
                                         'either 1, 3 or 4 channels on axis ' + str(self.channel_axis) + '. '
                                                                                                         'However, it was passed an array with shape ' + str(
                    x.shape) +
                ' (' + str(x.shape[self.channel_axis]) + ' channels).')

        if seed is not None:
            np.random.seed(seed)

        x = np.copy(x)
        if augment:
            ax = np.zeros(tuple([rounds * x.shape[0]] + list(x.shape)[1:]), dtype=K.floatx())
            for r in range(rounds):
                for i in range(x.shape[0]):
                    ax[i + r * x.shape[0]] = self.random_transform(x[i])
            x = ax

        if self.featurewise_center:
            self.mean = np.mean(x, axis=0)
            x -= self.mean

        if self.featurewise_std_normalization:
            self.std = np.std(x, axis=0)
            x /= (self.std + K.epsilon())

        if self.zca_whitening:
            flat_x = np.reshape(x, (x.shape[0], x.shape[1] * x.shape[2] * x.shape[3]))
            sigma = np.dot(flat_x.T, flat_x) / flat_x.shape[0]
            u, s, _ = linalg.svd(sigma)
            self.principal_components = np.dot(np.dot(u, np.diag(1. / np.sqrt(s + self.zca_epsilon))), u.T)