"m x n" dimensional gradient-style array in Python

I checked out gradient descent using python and numpy but it didn't solve my problem.

I'm trying to get familiar with image-processing and I want to generate a few test arrays to mess around with in Python.

Is there a method (like np.arange) to create a m x n array where the inner entries form some type of gradient?

I did an example of a naive method for generating the desired output.

Excuse my generality of the term gradient, I'm using it in it's simple meaning as smooth transition in color.

#!/usr/bin/python
import numpy as np
import matplotlib.pyplot as plt

#Set up parameters
m = 15
n = 10
A_placeholder = np.zeros((m,n))
V_m = np.arange(0,m).astype(np.float32)
V_n = np.arange(0,n).astype(np.float32)

#Iterate through combinations
for i in range(m):
    m_i = V_m[i]
    for j in range(n):
        n_j = V_n[j]
        A_placeholder[i,j] = m_i * n_j #Some combination

#Relabel
A_gradient = A_placeholder
A_placeholder = None

#Print data
print A_gradient
#[[   0.    0.    0.    0.    0.    0.    0.    0.    0.    0.]
 [   0.    1.    2.    3.    4.    5.    6.    7.    8.    9.]
 [   0.    2.    4.    6.    8.   10.   12.   14.   16.   18.]
 [   0.    3.    6.    9.   12.   15.   18.   21.   24.   27.]
 [   0.    4.    8.   12.   16.   20.   24.   28.   32.   36.]
 [   0.    5.   10.   15.   20.   25.   30.   35.   40.   45.]
 [   0.    6.   12.   18.   24.   30.   36.   42.   48.   54.]
 [   0.    7.   14.   21.   28.   35.   42.   49.   56.   63.]
 [   0.    8.   16.   24.   32.   40.   48.   56.   64.   72.]
 [   0.    9.   18.   27.   36.   45.   54.   63.   72.   81.]
 [   0.   10.   20.   30.   40.   50.   60.   70.   80.   90.]
 [   0.   11.   22.   33.   44.   55.   66.   77.   88.   99.]
 [   0.   12.   24.   36.   48.   60.   72.   84.   96.  108.]
 [   0.   13.   26.   39.   52.   65.   78.   91.  104.  117.]
 [   0.   14.   28.   42.   56.   70.   84.   98.  112.  126.]]

#Show Image
plt.imshow(A_gradient)
plt.show()

I've tried np.gradient but it didn't give me the desired output.

#print np.gradient(np.array([V_m,V_n]))
#Traceback (most recent call last):
#    File "Untitled.py", line 19, in <module>
#        print np.gradient(np.array([V_m,V_n]))
#    File "/Users/Mu/anaconda/lib/python2.7/site-packages/numpy/lib/function_base.py", line 1458, in gradient
#        out[slice1] = (y[slice2] - y[slice3])
#ValueError: operands could not be broadcast together with shapes (10,) (15,)