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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 generate(self):
 """ Generator for creating the cross-validation slices. Returns A tuple of that contains two fixmats (training and test) and two Category objects (test and train). """

        for _ in range(0, self.num_slices): 
            #1. separate fixmat into test and training fixmat
            subjects = np.unique(self.fm.SUBJECTINDEX)
            test_subs  = randsample(subjects,
                            self.subject_hold_out*len(subjects))  
            train_subs = [x for x in subjects if x not in test_subs] 
            test_fm  = self.fm[ismember(self.fm.SUBJECTINDEX, test_subs)]
            train_fm = self.fm[ismember(self.fm.SUBJECTINDEX, train_subs)]
            
            #2. distribute images 
            test_imgs = {}
            train_imgs = {}
            id_test = (test_fm.x <1) & False
            id_train = (train_fm.x <1)  & False
            for cat in self.categories:
                imgs = cat.images()
                test_imgs.update({cat.category:randsample(imgs,
                                self.image_hold_out*len(imgs)).tolist()})
                train_imgs.update({cat.category:[x for x in imgs 
                                if not ismember(x, test_imgs[cat.category])]})
                id_test = id_test | ((ismember(test_fm.filenumber, 
                                test_imgs[cat.category])) & 
                                (test_fm.category == cat.category))
                id_train = id_train | ((ismember(train_fm.filenumber, 
                                train_imgs[cat.category])) & 
                                (train_fm.category == cat.category))


            #3. Create categories objects and yield result
            test_stimuli = Categories(self.categories.loader, test_imgs,
                                      features=self.categories._features,
                                      fixations=test_fm)
            train_stimuli = Categories(self.categories.loader, train_imgs,
                                       features=self.categories._features,
                                       fixations=train_fm)
            yield (train_fm[id_train], 
                   train_stimuli, 
                   test_fm[id_test], 
                   test_stimuli)