ValueError: Shapes must be equal rank in assign_add() - tensorflow2.0

Question

I am reading tf.Variable in Tensorflow r2.0 in TF2:
import tensorflow as tf
# Create a variable.
w = tf.constant([1, 2, 3, 4], tf.float32, shape=[2, 2])
# Use the variable in the graph like any Tensor.
y = tf.matmul(w,tf.constant([7, 8, 9, 10], tf.float32, shape=[2, 2]))
v= tf.Variable(w)
# The overloaded operators are available too.
z = tf.sigmoid(w + y)
tf.shape(z)
# Assign a new value to the variable with `assign()` or a related method.
v.assign(w + 1)
v.assign_add(tf.constant([1.0, 21]))
ValueError: Shapes must be equal rank, but are 2 and 1 for
'AssignAddVariableOp_4' (op: 'AssignAddVariableOp') with input shapes:
[], 2.
And also how come the following returns false?
tf.shape(v) == tf.shape(tf.constant([1.0, 21],tf.float32))
My other question is that when we are in TF 2, we should not use tf.Session() anymore, correct? It seems we should never run session.run(), but the API document keys doing it with tf.compat.v1, etc. So why they are using it in TF2 docs?
Any help would be appreciated.
CS

Answer 1

As it clearly says in the error, it is expecting shape [2,2] for assign_add on v which is having the shape [2,2].
If you try to give any shape other than the initial shape of the Tensor which you are trying to do assign_add the error will be given.
Below is the modified code with the expected shape for the operation.
import tensorflow as tf
# Create a variable.
w = tf.constant([1, 2, 3, 4], tf.float32, shape=[2, 2])
# Use the variable in the graph like any Tensor.
y = tf.matmul(w,tf.constant([7, 8, 9, 10], tf.float32, shape=[2, 2]))
v= tf.Variable(w)
# The overloaded operators are available too.
z = tf.sigmoid(w + y)
tf.shape(z)
# Assign a new value to the variable with `assign()` or a related method.
v.assign(w + 1)
print(v)
v.assign_add(tf.constant([1, 2, 3, 4], tf.float32, shape=[2, 2]))
Output for v:
<tf.Variable 'UnreadVariable' shape=(2, 2) dtype=float32, numpy=
array([[3., 5.],
[7., 9.]], dtype=float32)>
Now the following Tensor comparison is returning True.
tf.shape(v) == tf.shape(tf.constant([1.0, 21],tf.float32))
<tf.Tensor: shape=(2,), dtype=bool, numpy=array([ True, True])>
Coming to your tf.Session() question, in TensorFlow 2.0 eager execution is enabled by default, still, if you need to disable eager execution and can use tf.Session like below.
import tensorflow as tf
tf.compat.v1.disable_eager_execution()
hello = tf.constant('Hello, TensorFlow!')
sess = tf.compat.v1.Session()
print(sess.run(hello))