human
Solve the following python programming problem given the following code:
# Creating an array of all zeroes

np.zeros((3,4),int)

print('a. np.zeros((2,2))')
a = np.zeros((2,2),int)  
print(a)                 

print('b. np.ones((1,2))')
b = np.ones((1,2))   
print(b)              

print('c. np.full((2,2), 7)')
c = np.full((2,2), 7) # Create a constant array
print(c)              

print('d. np.eye(2)')
d = np.eye(2)        
print(c)             
    
print('e. np.random.random((2,2))')
e = np.random.random((2,2)) 
print(e)                    
Given the above code, generate some markdown instructions for it.
assistant
#Here is the Python program you requested.

Recap from last week

We looked at the basics of Object Oriented Programming, or OOP, last week. If you couldn't make it, don't be concerned because the content from last week won't affect what we will be looking at today.
```
class Human(object):
    def init(self,name,age,height):
        self.name = name
        self.age = age
        self.height = height
def __lt__(self,other):
    return self.age &lt; other.age
def __le__(self,other):
    return self.age &lt;= other.age
def __gt__(self,other):
    return self.age &gt; other.age
def __ge__(self,other):
    return self.age &gt;= other.age
def __eq__(self,other):
    return self.age==other.age

def age_in_dog_years(self):
    return 7*self.age

```
NumPy
NumPy is the standard mathematical and scientific computing package for Python. NumPy is a need to know if you want to write efficient and interpretable code. NumPy includes an optimised array type as well as linear algebra, Fourier Transform and random number capabilities.
Under the hood, much of NumPy is written in C/C++/Fortran which is highly optimised according to your specific computer. Using NumPy gives you features and speed you couldn't achieve with native Python.
Link to NumPy documentation
The main NumPy feature - the Array type
The NumPy array is a grid of values all of the same type, this is different to Python lists which can have elements of different types. 
Arrays are indexed similarly to lists, with each dimension being indexed from zero. When declaring the Array object be clear in your mind the dimensions of the array you need.
np.ones((3,4),int) 
    -&gt; 3 is the number of ROWS
    -&gt; 4 is the number of COLUMNS
    -&gt; int is the type of the array elements
--
array([[1, 1, 1, 1],
       [1, 1, 1, 1],
       [1, 1, 1, 1]])