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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, y, verbosity=0):
 """Fit the model according to the given training data. Parameters X : List of ndarrays, one for each training example. Each training example's shape is (string1_len, string2_len, n_features), where string1_len and string2_len are the length of the two training strings and n_features the number of features. y : array-like, shape (n_samples,) Target vector relative to X. Returns ------- self : object Returns self. """

        self.classes = list(set(y))
        n_points = len(y)
        if len(X) != n_points:
            raise Exception('Number of training points should be the same as training labels.')

        if not self._state_machine:
            self._state_machine = DefaultStateMachine(self.classes)

        # Initialize the parameters given the state machine, features, and target classes.
        self.parameters = self._initialize_parameters(self._state_machine, X[0].shape[2])

        # Create a new model object for each training example
        models = [_Model(self._state_machine, x, ty) for x, ty in zip(X, y)]

        self._evaluation_count = 0

        def _objective(parameters):
            gradient = np.zeros(self.parameters.shape)
            ll = 0.0  # Log likelihood
            # TODO: Embarrassingly parallel
            for model in models:
                dll, dgradient = model.forward_backward(parameters.reshape(self.parameters.shape))
                ll += dll
                gradient += dgradient

            parameters_without_bias = np.array(parameters, dtype='float64')  # exclude the bias parameters from being regularized
            parameters_without_bias[0] = 0
            ll -= self.l2_regularization * np.dot(parameters_without_bias.T, parameters_without_bias)
            gradient = gradient.flatten() - 2.0 * self.l2_regularization * parameters_without_bias

            if verbosity > 0:
                if self._evaluation_count == 0:
                    print('{:10} {:10} {:10}'.format('Iteration', 'Log-likelihood', '|gradient|'))
                if self._evaluation_count % verbosity == 0:
                    print('{:10} {:10.4} {:10.4}'.format(self._evaluation_count, ll, (abs(gradient).sum())))
            self._evaluation_count += 1

            # TODO: Allow some of the parameters to be frozen. ie. not trained. Can later also completely remove
            # TODO:     the computation associated with these parameters.
            return -ll, -gradient

        def _objective_copy_gradient(paramers, g):
            nll, ngradient = _objective(paramers)
            g[:] = ngradient
            return nll

        if self._optimizer:
            self.optimizer_result = self._optimizer(_objective, self.parameters.flatten(), **self._optimizer_kwargs)
            self.parameters = self.optimizer_result[0].reshape(self.parameters.shape)
        else:
            optimizer = lbfgs.LBFGS()
            final_betas = optimizer.minimize(_objective_copy_gradient,
                                             x0=self.parameters.flatten(),
                                             progress=None)
            self.optimizer_result = final_betas
            self.parameters = final_betas.reshape(self.parameters.shape)
        return self