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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 calc_gradient_norm_for_replicates(self, replicates='bootstrap', ridge=None, constrained_pos=None, weights=None):
 """ Calculate the Euclidean-norm of the gradient of one's replicates, given one's dataset. Parameters replicates : str in {'bootstrap', 'jackknife'}. Denotes which set of replicates should have their log-likelihoods calculated. ridge : float or None, optional. Denotes the ridge penalty used when estimating the replicates, and to be used when calculating the gradient. If None, no ridge penalty is used. Default == None. constrained_pos : list or None, optional. Denotes the positions of the array of estimated parameters that are not to change from their initial values. If a list is passed, the elements are to be integers where no such integer is greater than `self.mle_params` Default == None. weights : 1D ndarray or None, optional. Allows for the calculation of weighted log-likelihoods. The weights can represent various things. In stratified samples, the weights may be the proportion of the observations in a given strata for a sample in relation to the proportion of observations in that strata in the population. In latent class models, the weights may be the probability of being a particular class. Returns ------- log_likelihoods : 1D ndarray. Each element stores the log-likelihood of the associated parameter values on the model object's dataset. The log-likelihoods are also stored on the `replicates + '_log_likelihoods'` attribute. """

        # Check the validity of the kwargs
        ensure_replicates_kwarg_validity(replicates)
        # Create the estimation object
        estimation_obj =\
            create_estimation_obj(self.model_obj,
                                  self.mle_params.values,
                                  ridge=ridge,
                                  constrained_pos=constrained_pos,
                                  weights=weights)
        # Prepare the estimation object to calculate the gradients
        if hasattr(estimation_obj, "set_derivatives"):
            estimation_obj.set_derivatives()
        # Get the array of parameter replicates
        replicate_array = getattr(self, replicates + "_replicates").values
        # Determine the number of replicates
        num_reps = replicate_array.shape[0]
        # Initialize an empty array to store the gradient norms
        gradient_norms = np.empty((num_reps,), dtype=float)

        # Create an iterable for iteration
        iterable_for_iteration = PROGRESS(xrange(num_reps),
                                          desc="Calculating Gradient Norms",
                                          total=num_reps)

        # Iterate through the rows of the replicates and calculate and store
        # the gradient norm for each replicated parameter vector.
        for row in iterable_for_iteration:
            current_params = replicate_array[row]
            gradient = estimation_obj.convenience_calc_gradient(current_params)
            gradient_norms[row] = np.linalg.norm(gradient)
        return gradient_norms