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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 _inner_parameters_changed(self, kern, X, Z, likelihood, Y, Y_metadata, Lm=None, dL_dKmm=None, psi0=None, psi1=None, psi2=None, **kwargs):
 """ This is the standard part, which usually belongs in parameters_changed. For automatic handling of subsampling (such as missing_data, stochastics etc.), we need to put this into an inner loop, in order to ensure a different handling of gradients etc of different subsets of data. The dict in current_values will be passed aroung as current_values for the rest of the algorithm, so this is the place to store current values, such as subsets etc, if necessary. If Lm and dL_dKmm can be precomputed (or only need to be computed once) pass them in here, so they will be passed to the inference_method. subset_indices is a dictionary of indices. you can put the indices however you like them into this dictionary for inner use of the indices inside the algorithm. """

        return self.inference_method.inference(kern, X, Z, likelihood, Y, Y_metadata, Lm=Lm,
                                               dL_dKmm=dL_dKmm, psi0=psi0, psi1=psi1, psi2=psi2, **kwargs)