How to use TensorDataset, Dataloader (pytorch)

import numpy as np
import torch
from torch.utils.data import TensorDataset
from torch.utils.data import DataLoader

input = np.random.rand(4, 2) # Input data
correct = np.random.rand(4, 1) # Correct answer data

input = torch.FloatTensor(input) # Change to an array that can be handled by pytorch
correct = torch.FloatTensor(correct) # Same as above

print(input)

⇒ tensor([[0.7752, 0.9332],
              [0.5186, 0.1956],
              [0.1267, 0.1171],
              [0.3495, 0.5235]])

print(correct)

⇒ tensor([[0.2506],
              [0.9407],
              [0.9416],
              [0.8879]])

dataset = TensorDataset(input, correct) # set the data
print(vars(dataset)) # vars prints the contents of the object

⇒{'tensors': (tensor([[0.7752, 0.9332], # Input data
                            [0.5186, 0.1956],
                            [0.1267, 0.1171],
                            [0.3495, 0.5235]]),
                    tensor([[0.2506], # Correct answer data
                            [0.9407],
                            [0.9416],
                            [0.8879]]))}

train_load = DataLoader(dataset, batch_size=2, shuffle=False) # Data shuffle with shuffle=True

for x, t in train_load:
    print(x)
    print(t)

⇒ tensor([[0.7752, 0.9332], # First read of x
              [0.5186, 0.1956]])
　tensor([[0.2506], # First read of t
              [0.9407]])

   tensor([[0.1267, 0.1171], # 2nd read
              [0.3495, 0.5235]])
　tensor([[0.9416],
              [0.8879]])