Tensor Broadcasting, Adding, Multiplying, Reshaping

Paste the code below (but not the output section) into a Colab (https:\\colab.research.google.com\) Jupyter Notebook,if you would like to execute it.
-------------------------------------------------------------------------------------------------

# Broadcasting - enlarging a tensor to match another one, but repeating data.
###############################################################################
import numpy as np

x1 = np.array([1,2,3])

print('x1: '); print (x1)
print('x1.ndim:  ', x1.ndim)
print('x1.shape: ', x1.shape)

x2 = np.array([[1,2,3],[4,5,6]])
print('x2: ') ; print (x2)
print('x2.ndim:  ', x2.ndim)
print('x2.shape: ', x2.shape)

x3 = x1 + x2

print('x3: '); print(x3)
print('x3.ndim:  ', x3.ndim)
print('x3.shape: ', x3.shape)

# Vector Dot Product
###############################################################################
v1 = np.array([1,2,3])
v2 = np.array([1,2,3])
v3 = v1.dot(v2)
print('v3: ', v3)

# Matrix - Vector Multiplication
###############################################################################
m1 = np.array([[1,2,3],[4,5,6],[7,8,9]])
v1 = np.array([10,11,12])
v2 = m1.dot(v1)
print('v2: ', v2)

# Tensor Multiplication
###############################################################################
t1 = np.array([[1,2,3],[4,5,6],[7,8,9]])
t2 = np.array([[10,11,12],[13,14,15],[16,17,18]])
t3 = t1.dot(t2)
print('t3: '); print(t3)

# Reshaping Tensors
###############################################################################
print("t3.shape: ", t3.shape)
t4 = t3.reshape(1,9)
print("t4"); print(t4)
print("t4.shape: ", t4.shape)
t5 = t4.reshape(9,)
print("t5"); print(t5)
print("t5.shape: ", t5.shape)
t6 = t4.reshape(9,1)
print("t6"); print(t6)
print("t6.shape: ", t6.shape)

-------------------------------------------------------------------------------
-- Output
-------------------------------------------------------------------------------
1: 
[1 2 3]
x1.ndim:   1
x1.shape:  (3,)
x2: 
[[1 2 3]
 [4 5 6]]
x2.ndim:   2
x2.shape:  (2, 3)
x3: 
[[2 4 6]
 [5 7 9]]
x3.ndim:   2
x3.shape:  (2, 3)
v3:  14
v2:  [ 68 167 266]
t3: 
[[ 84  90  96]
 [201 216 231]
 [318 342 366]]
t3.shape:  (3, 3)
t4
[[ 84  90  96 201 216 231 318 342 366]]
t4.shape:  (1, 9)
t5
[ 84  90  96 201 216 231 318 342 366]
t5.shape:  (9,)
t6
[[ 84]
 [ 90]
 [ 96]
 [201]
 [216]
 [231]
 [318]
 [342]
 [366]]
t6.shape:  (9, 1)