Python Unit Test Writer

def quick_sort(arr): 
  if len(arr) <= 1: 
    return arr 
  pivot = arr[len(arr) // 2] 
  left = [x for x in arr if x < pivot] 
  right = [x for x in arr if x > pivot] 
  middle = [x for x in arr if x == pivot] 
  return quick_sort(left) + middle + quick_sort(right)

import numpy as np 
 def sigmoid(x): 
   return 1/(1 + np.exp(-x)) 
 def sigmoid_derivative(x): 
   return x * (1 - x)

SQL Query: 
 SELECT Customers.CustomerName, Orders.OrderID 
 FROM Customers 
 FULL OUTER JOIN Orders 
 ON Customers.CustomerID=Orders.CustomerID 
 ORDER BY Customers.CustomerName

## R code 
 library(ggplot2) 
 p <- ggplot(mtcars, aes(mpg, wt, colour = factor(cyl))) 
 p + geom_point()

def binary_search_recursive(arr, element, start=0, end=None): 
  if end is None: 
    end = len(arr) - 1 
  if start > end: 
    return False 
  mid = (start + end) // 2 
  if element == arr[mid]: 
    return mid 
  if element < arr[mid]: 
    return binary_search_recursive(arr, element, start, mid-1) 
  else: 
    return binary_search_recursive(arr, element, mid+1, end)

## DAX Code 
 VAR base_table = 
    CALCULATETABLE ( 
        SUMMARIZE ( Sales, Customers[CustomerName], 'Date'[Year], "SalesAmount", SUM ( Sales[SalesAmount] ) ), 
        ALL ( 'Date' ) 
    ) 
 RETURN 
    CALCULATE ( sum ( base_table[SalesAmount]), FILTER ( ALL ( Customers[CustomerName] ), Customers[CustomerName] = MAX ( base_table[CustomerName] ) ) )

## Microsoft Excel Formula: 
 =VLOOKUP(B2,$G$3:$I$22,3,FALSE)

def decorator_func(func): 
  def wrapper_func(*args, **kwargs): 
    print('Before executing', func.__name__) 
    result = func(*args, **kwargs) 
    print('After executing', func.__name__) 
    return result 
  return wrapper_func 
 @decorator_func 
 def display_info(name, age): 
  print('display_info ran with arguments ({}, {})'.format(name, age))

import pandas as pd 
 def missing_values_table(df): 
        mis_val = df.isnull().sum() 
        mis_val_percent = 100 * df.isnull().sum()/len(df) 
        mis_val_table = pd.concat([mis_val, mis_val_percent], axis=1) 
        return mis_val_table.reindex(mis_val_table[mis_val_percent].sort_values(ascending=False).index)

class SingletonMeta(type): 
    _instances = {} 
    def __call__(cls, *args, **kwargs): 
        if cls not in cls._instances: 
            cls._instances[cls] = super(SingletonMeta, cls).__call__(*args, **kwargs) 
        return cls._instances[cls] 
 class Singleton(metaclass=SingletonMeta): 
    pass