I have a  dataset of 3-D(time_stepinputsizetotal_num) matrix which is a .mat file. I want to use  DataLoader to get a input dataset for LSTM which batch_size is 5. My code is as following:
file_path = "…/database/frameLength100/notOverlap/a.mat"
mat_data = s.loadmat(file_path)
tensor_data = torch.from_numpy(mat_data[‘a’])   #Tensor
class CustomDataset(Dataset):
def __init__(self, tensor_data):
    self.tensor_data = tensor_data

def __getitem__(self, index):
    data = self.tensor_data[index]
    label = 1;
    return data, label

def __len__(self):
    return len(self.tensor_data)

custom_dataset = CustomDataset(tensor_data=tensor_data)
train_loader = DataLoader(dataset=custom_dataset, batch_size=5, shuffle=True)
I think the code is wrong but I have no idea how to correct it. What makes me confused is how how can I make DataLoader know which dimension is ‘total_num’ so that I get the dataset which batch size is 5.