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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, pairs, y, calibration_params=None):
 """Learn the MMC model. The threshold will be calibrated on the trainset using the parameters `calibration_params`. Parameters pairs : array-like, shape=(n_constraints, 2, n_features) or (n_constraints, 2) 3D Array of pairs with each row corresponding to two points, or 2D array of indices of pairs if the metric learner uses a preprocessor. y : array-like, of shape (n_constraints,) Labels of constraints. Should be -1 for dissimilar pair, 1 for similar. calibration_params : `dict` or `None` Dictionary of parameters to give to `calibrate_threshold` for the threshold calibration step done at the end of `fit`. If `None` is given, `calibrate_threshold` will use the default parameters. Returns ------- self : object Returns the instance. """

    calibration_params = (calibration_params if calibration_params is not
                          None else dict())
    self._validate_calibration_params(**calibration_params)
    self._fit(pairs, y)
    self.calibrate_threshold(pairs, y, **calibration_params)
    return self