import torch.nn.functional as F
import torch
import sys
sys.path.insert(1,
    '/Users/user/Downloads/deep-learning-v2-pytorch-master/intro-to-pytorch/')
import helper
from torch import nn
from torchvision import datasets, transforms
x = torch.randn(2, 2, requires_grad=True)
print(x)
y = x ** 2
print(y)
print(y.grad_fn)
z = y.mean()
print(z)
print(x.grad_fn)
z.backward()
print(x.grad)
print(x / 2)

73fe6b0baaa733d020f21c7445027a29809e51f5
<mask token>

<mask token>
sys.path.insert(1,
    '/Users/user/Downloads/deep-learning-v2-pytorch-master/intro-to-pytorch/')
<mask token>
print(x)
<mask token>
print(y)
print(y.grad_fn)
<mask token>
print(z)
print(x.grad_fn)
z.backward()
print(x.grad)
print(x / 2)

<mask token>
sys.path.insert(1,
    '/Users/user/Downloads/deep-learning-v2-pytorch-master/intro-to-pytorch/')
<mask token>
x = torch.randn(2, 2, requires_grad=True)
print(x)
y = x ** 2
print(y)
print(y.grad_fn)
z = y.mean()
print(z)
print(x.grad_fn)
z.backward()
print(x.grad)
print(x / 2)

import torch.nn.functional as F
import torch
import sys
sys.path.insert(1,
    '/Users/user/Downloads/deep-learning-v2-pytorch-master/intro-to-pytorch/')
import helper
from torch import nn
from torchvision import datasets, transforms
x = torch.randn(2, 2, requires_grad=True)
print(x)
y = x ** 2
print(y)
print(y.grad_fn)
z = y.mean()
print(z)
print(x.grad_fn)
z.backward()
print(x.grad)
print(x / 2)
