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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_tfa_inner( self, data, R, template_centers, template_widths, template_centers_mean_cov, template_widths_mean_var_reci):
 """Fit TFA model, the inner loop part Parameters data: 2D array, in shape [n_voxel, n_tr] The fMRI data of a subject R : 2D array, in shape [n_voxel, n_dim] The voxel coordinate matrix of fMRI data template_centers: 1D array The template prior on centers template_widths: 1D array The template prior on widths template_centers_mean_cov: 2D array, with shape [K, cov_size] The template prior on covariance of centers' mean template_widths_mean_var_reci: 1D array The reciprocal of template prior on variance of widths' mean Returns ------- TFA Returns the instance itself. """

        nfeature = data.shape[0]
        nsample = data.shape[1]
        feature_indices =\
            np.random.choice(nfeature, self.max_num_voxel, replace=False)
        sample_features = np.zeros(nfeature).astype(bool)
        sample_features[feature_indices] = True
        samples_indices =\
            np.random.choice(nsample, self.max_num_tr, replace=False)
        curr_data = np.zeros((self.max_num_voxel, self.max_num_tr))\
            .astype(float)
        curr_data = data[feature_indices]
        curr_data = curr_data[:, samples_indices].copy()
        curr_R = R[feature_indices].copy()
        centers = self.get_centers(self.local_prior)
        widths = self.get_widths(self.local_prior)
        unique_R, inds = self.get_unique_R(curr_R)
        F = self.get_factors(unique_R, inds, centers, widths)
        W = self.get_weights(curr_data, F)
        self.local_posterior_, self.total_cost = self._estimate_centers_widths(
            unique_R, inds, curr_data, W, centers, widths,
            template_centers, template_centers_mean_cov,
            template_widths, template_widths_mean_var_reci)

        return self