Hello everyone, I have just started learning Pytorch and i got a problem while trying to create dataloader from my customized datasets  which is contains 20 files “data_x.mat” stored in specific folder, I need to use them with data-loader, can anyone help me how to create this class to get iterable batches :
Mydata is

10 files each is dict {“Training_Patches”: shape(760,120,21,21)  /  “Label”: shape(1,760)}  stored in .mat file
10 files each is dict {“Testing_Patches”: shape(420,120,21,21)  /  “Label”: shape(1,420)}  stored in .mat file
Here is what i thing it should looks like, Any suggestion,ideas, help would be Appreciated

class Datasets(Dataset):
    def __init__(self):
        self.tensors = []
        self.labels = []
       for i  in range(len(data_i["Training_patches"])):
                    self.tensors.append(data_i["Training_patches"])
                    self.labels.append(data_i["labels"])

    def __getitem__(self, index):
        # return one item on the index
        return

    def __len__(self):
        # return the data length
        return