# Define a function to generate numbers using a generator
def generate_numbers(n):
    # Use a for loop to iterate through the range of numbers
    for i in range(n):
        # Use the yield keyword to return the current number
        yield i

# Instead of creating a list of numbers using the range function, use the generate_numbers function to create a generator object
my_generator = generate_numbers(10000)

# Use a for loop to iterate through the generator object and print each number
for num in my_generator:
    print(num)

# By using a generator instead of a list, we are creating fewer objects overall and optimizing garbage collection by generating less memory.

# Import the necessary modules
import os # Import the os module to access operating system functionalities
from contextlib import closing # Import the closing function from contextlib module to use as a context manager

# Define a function to delete a file
def delete_file(filename):
    with open(filename, 'w') as f: # Open the file in write mode and assign it to the variable f
        # Do something with file...
        # This code segment is missing the actual code to be executed, so it should be added here
        f.write("This is a test file.") # Example code to write to the file
    with closing(open(filename, 'r')) as f: # Open the file in read mode and assign it to the variable f
        # Read from the same file without creating a new one!
        # This code segment is missing the actual code to be executed, so it should be added here
        print(f.read()) # Example code to read from the file
    os.remove(filename) # Remove the file from the operating system

# Call the function with a sample filename
delete_file("test.txt") # This will create a file named "test.txt", write to it, read from it, and then delete it.

# Importing the necessary module for weak references
from weakref import ref

# Defining a function to create an expensive object
def create_expensive_object():
    expensive = "This is an expensive object"
    return expensive

# Defining a function to get a weak reference to the expensive object
def get_expensive_object():
    # Creating the expensive object
    expensive = create_expensive_object()
    # Returning a weak reference to the expensive object
    return ref(expensive)

# Later...
# Checking if the weak reference still points to the expensive object
if my_weak_reference().__get__(None):
    # If the object is still available, use it
    print("Using the expensive object")
else:
    # If the object has been garbage collected, recreate it
    print("Recreating the expensive object")

# Import the necessary modules
import cProfile # Import the cProfile module for profiling
from pstats import Stats # Import the Stats module for analyzing the profiling results

# Define a function to be profiled
def my_function():
    # Do something expensive...
    # This function does not have any code inside, so it will not be profiled properly
    # Add some code here to be profiled

# Run the profiler on the defined function
cProfile.run('my_function()')

# Create a Stats object to analyze the profiling results
s = Stats('profile.txt')

# Sort the results by the time taken for each function call
s.sort_stats('time')

# Print the top 10 functions with the longest execution time
s.print_stats(10)

# Import the necessary modules
import gc # Import the garbage collector module
from pstats import Stats # Import the Stats module from pstats

# Define a function that creates temporary objects
def my_function():
    # Do something that creates lots of temporary objects...

# Set the garbage collector to debug mode
gc.set_debug(gc.DEBUG_STATS)

# Create an infinite loop
while True:
    my_function() # Call the function to create temporary objects
    del gc.get_objects() # Clear the list of objects for a fresh start
    s = Stats('profile.txt') # Create a Stats object with the specified file
    s.sort_stats('time').print_stats(10) # Sort and print the top 10 objects based on time

# The purpose of this script is to use a garbage collector profiler to identify which objects are being collected and how often. The gc module is used to set the debug mode and clear the list of objects. The Stats module is used to create a Stats object and sort and print the top 10 objects based on time. The my_function() is called within the loop to create temporary objects for the garbage collector to track.