Which piece of code would apply stochastic gradient descent for $10$ iterations on the

formula: $y=x^{****}2+x,$ where $x$ is a tensor of shape $(1,2)$

A:

$x=tf.$Variable$($tf$.$random.uniform$([1,2]))$

optimizer $=$ tf$.$optimizers.SGD$()$

for $i$ in range $(0,10)$ :

with tf$.$GradientTape$()$ as tape:

$y=tf.$add$(tf.(x),x)$

optimizer.apply$\_$gradients$([($gradients$,$ x$)])$

gradients $=$ tape. gradient$(y,x)$

B:

$x=tf.$ Variable $($tf$.$random.uniform$([2,1])$

optimizer $=$ tf$.$optimizers.SGD$()$

for $i$ in range $(,10)$ :

with tf$.$GradientTape$()$ as tape:

$y=tf*add(x,x)$

gradients $=$ tape. gradient$(y,x)$

optimizer.apply$\_$gradients$([($gradients$,$ x$)])$

C:

$x=tf.$ Variable$($tf$.$random.uniform$([1,2]))$

optimizer $=$ tf$.$optimizers.SGD$()$

for $i$ in range $(,10)$ :

with tf$.$GradientTape$()$ as tape:

$y=tf*add(tf.(x),x)$

gradients $=$ tape. gradient$(y,x)$

optimizer.apply$\_$gradients$([($gradients$,$ x$)])$