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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 get_dim(self, X):
 """ Get the output dimensionality of this basis. This makes a cheap call to transform with the initial parameter values to ascertain the dimensionality of the output features. Parameters X : ndarray (N, d) array of observations where N is the number of samples, and d is the dimensionality of X. Returns ------- int : The dimensionality of the basis. """

        # Cache
        if not hasattr(self, '_D'):
            self._D = self.transform(X[[0]], *self.params_values()).shape[1]
        return self._D