Hi, I have 2 questions:

- In question 3: as I understood, derivative of a vector over a vector results in a matrix, since f is a vector of a, I got a vector instead, which I dont understand.
- In question 5: I have a small problem: my intention is to plot the x_grad after GradientTape, I tried but still error. Hope sb can help me out.

import matplotlib.pyplot as plt

import numpy as np

x = tf.range(-10,10,1,dtype=tf.float32)

x = tf.Variable(x)

with tf.GradientTape() as t:

y = np.sin(x)

x_grad = t.gradient(y, x)

#Plotting

x = np.arange(-10,10,0.1)

plt.figure(1)

plt.plot(x, np.sin(x), ‘r’)

plt.plot(x,np.(x_grad),‘g’)

plt.show()

#Even when I try to assign value to y, I got None result

y = tf.Variable(tf.zeros_like(x))

with tf.GradientTape() as t:

for i in range(tf.size(y)):

y[i].assign(math.sin(x[i]))

Thank you

For question 5, you can plot ‘x_grad’ directly through pl.plt without np.

For question 5, I also ran into some similar problems, like:

```
TypeError: ResourceVariable doesn't have attribute ....
Y is None object.
```

I think the reason is that numpy ndarrays are different from tensors and they have different attributes, thus we can’t mix them up. For example, when you serve y as a numpy array in tf.gradient(y,x) method, it will return a None Object.

Also, you can refer to my code below:

```
import tensorflow as tf
import matplotlib.pyplot as plt
x = tf.range(-10, 10, 0.1)
x = tf.Variable(x)
with tf.GradientTape() as t:
y = tf.math.sin(x)
x_grad = t.gradient(y, x)
# plotting
x = np.arange(-10, 10, 0.1)
plt.figure(1)
plt.plot(x, np.sin(x), color='r')
plt.plot(x, x_grad.numpy(), color='g')
plt.show()
```

It works on my local machine.