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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, sequences, y=None):
 """Fit the model with a collection of sequences. This method is not online. Any state accumulated from previous calls to fit() or partial_fit() will be cleared. For online learning, use `partial_fit`. Parameters sequences: list of array-like, each of shape (n_samples_i, n_features) Training data, where n_samples_i in the number of samples in sequence i and n_features is the number of features. y : None Ignored Returns ------- self : object Returns the instance itself. """

        self._initialized = False
        check_iter_of_sequences(sequences, max_iter=3)  # we might be lazy-loading
        for X in sequences:
            self._fit(X)

        if self.n_sequences_ == 0:
            raise ValueError('All sequences were shorter than '
                             'the lag time, %d' % self.lag_time)

        return self