question:# Contextlib Advanced Context Manager Implementation Problem Description You are required to implement a context manager that logs the time taken to execute a block of code and ensures certain resources are closed properly, even if exceptions occur. 1. **TimeLoggingContextManager**: This context manager should log the start time when entering the block and log the elapsed time when exiting the block. 2. **ManagedResource**: A mock resource that must be acquired and released properly using `contextlib`. Task 1. Implement a `TimeLoggingContextManager` class that logs the start and end times of a `with` block. 2. Implement a generator-based context manager function `managed_resource` using the `@contextlib.contextmanager` decorator. This context manager should acquire a `ManagedResource` (simulate resource acquisition and release). Requirements 1. **TimeLoggingContextManager class**: - It should log the message `"Entering block"` and the current time when entering the block. - It should log the message `"Exiting block. Time taken: X seconds"` when exiting the block, where `X` is the time taken. 2. **managed_resource function**: - Use the `contextlib.contextmanager` decorator. - It should simulate acquiring a `ManagedResource` by printing `"Resource acquired"`. - Ensure the resource is released by printing `"Resource released"` even if an exception occurs in the block. Example Usage ```python import time from contextlib import contextmanager class TimeLoggingContextManager: def __enter__(self): self.start_time = time.monotonic() print("Entering block") return self def __exit__(self, exc_type, exc_val, exc_tb): end_time = time.monotonic() print(f"Exiting block. Time taken: {end_time - self.start_time} seconds") @contextmanager def managed_resource(): try: # Simulate resource acquisition print("Resource acquired") yield finally: # Ensure resource release print("Resource released") # Usage with TimeLoggingContextManager(), managed_resource(): print("Inside the with block") # Simulate work by sleeping time.sleep(2) ``` Input/Output - You don't need to take any input from the user. - The program should output the log messages as described when the `with` block is executed. Constraints - You must properly handle resource acquisition and release. - Ensure the timing logs are accurate to the context manager's usage. - Simulate exceptions and ensure the resource is still released correctly. Notes - Use the `time.monotonic()` function for measuring time intervals. - Ensure exception handling does not interfere with resource release.

question:# **Email Retrieval System Using POP3** You are tasked with implementing a program that connects to a POP3 email server, authenticates a user, retrieves specific email details, and safely terminates the connection. Your implementation should demonstrate a thorough understanding of Python's `poplib` module functionalities, error handling, and overall code robustness. **Function: retrieve_emails** **Objective**: Write a function `retrieve_emails(host, port, username, password)` that: 1. Connects to the provided POP3 server. 2. Authenticates the user using the provided username and password. 3. Retrieves a list of unique IDs for all email messages on the server without marking them as seen. 4. Retrieves and prints the headers of the first 5 messages (or fewer if less than 5 exist) identified by these unique IDs. 5. Handles potential errors gracefully and ensures the mailbox is properly unlocked and the connection is safely terminated. **Input Format** - `host` (str): The hostname of the POP3 server. - `port` (int): The port number of the POP3 server. - `username` (str): The username for authentication. - `password` (str): The password for authentication. **Output Format** - Print the headers of the first 5 emails. **Constraints** - Assume the host, port, username, and password are valid strings. - Ensure proper error handling for connection and authentication errors. - Ensure the connection is gracefully terminated even if an error occurs during any of the steps. **Example Usage** ```python retrieve_emails("pop.example.com", 110, "[email protected]", "password123") ``` This should print: ``` Email 1 Headers: [...] Email 2 Headers: [...] ... ``` **Note**: Substitute the headers with the actual retrieved headers from the emails. **Implementation Notes** 1. Use the `poplib.POP3` class from the `poplib` module. 2. Utilize the relevant methods (`user`, `pass_`, `uidl`, `top`, `quit`) as documented. 3. Include error handling for `poplib.error_proto` and other potential exceptions. 4. Ensure the program is robust and releases resources adequately.

question:**Memory Management with Custom Classes in Python** You have been tasked to implement a memory management system within a custom class architecture in Python to mimic some of the functionality described in the C API documentation. # Task: 1. **Define a CustomObject class** that: - Contains a constructor (`__init__`) that initializes the object, similar to the `PyObject_Init`. - Maintains a class-level counter that tracks the number of instances created and a list of active instances. 2. **Implement object allocation and deallocation:** - A `allocate` class method which mimics the allocation logic in `PyObject_New`. - A `deallocate` class method which mimics the deallocation logic in `PyObject_Del`. 3. **Include reference counting:** - Each instance should have a reference counter that increments when a new reference is made and decrements when a reference is deleted. If the reference count drops to zero, the object should be deallocated automatically. # Implementation Details: 1. **CustomObject Class:** - `__init__(self)`: Initializes the object and increases the count of active instances. - `ref_count`: An integer attribute to track the number of active references to the object. 2. **Class Methods:** - `allocate()`: A class method that creates a new instance of the class, increments the instance count, and adds the instance to a tracking list. - `deallocate(obj)`: A class method that removes an instance from the tracking list and handles necessary cleanup. 3. **Instance Methods:** - `add_reference(self)`: Increases the reference counter. - `del_reference(self)`: Decreases the reference counter and calls `deallocate(self)` if the counter reaches zero. # Example Usage: ```python class CustomObject: instance_count = 0 active_instances = [] def __init__(self): self.ref_count = 1 CustomObject.instance_count += 1 CustomObject.active_instances.append(self) print(f'Object created. Total instances: {CustomObject.instance_count}') @classmethod def allocate(cls): return cls() @classmethod def deallocate(cls, obj): CustomObject.active_instances.remove(obj) CustomObject.instance_count -= 1 print(f'Object deallocated. Total instances: {CustomObject.instance_count}') def add_reference(self): self.ref_count += 1 print(f'Object reference added. Current ref count: {self.ref_count}') def del_reference(self): self.ref_count -= 1 print(f'Object reference removed. Current ref count: {self.ref_count}') if self.ref_count == 0: CustomObject.deallocate(self) # Example Test Case obj1 = CustomObject.allocate() obj1.add_reference() obj1.del_reference() obj1.del_reference() ``` Expected Output: ``` Object created. Total instances: 1 Object reference added. Current ref count: 2 Object reference removed. Current ref count: 1 Object reference removed. Current ref count: 0 Object deallocated. Total instances: 0 ``` # Constraints: - Implement all functionalities as specified. - Make sure that the methods properly manage the allocation and deallocation of instances. Good luck!

question:# Categorical Data Visualization Using Seaborn You are given a dataset of tips collected from a restaurant, which contains the following columns: - `total_bill`: Numeric, Total bill amount (continuous variable). - `tip`: Numeric, Tip amount (continuous variable). - `sex`: Categorical, Sex of the person paying the bill (`Male`, `Female`). - `smoker`: Categorical, Whether the person is a smoker (`Yes`, `No`). - `day`: Categorical, Day of the week (`Thur`, `Fri`, `Sat`, `Sun`). - `time`: Categorical, Time of day (`Lunch`, `Dinner`). - `size`: Numeric, Size of the party. Your task is to write Python code using seaborn to create the following visualizations and save them as images. 1. **Scatter Plot**: - Create a scatter plot showing the relationship between `day` and `total_bill`. - Add hue semantics to differentiate between smokers and non-smokers. - Save the plot as `scatter_plot.png`. 2. **Box Plot**: - Create a box plot to show the distribution of `total_bill` for each day of the week. - Add hue semantics to differentiate between lunch and dinner times. - Save the plot as `box_plot.png`. 3. **Bar Plot**: - Create a bar plot to show the average `tip` amount for each day of the week. - Add a confidence interval of 95% around the estimates. - Save the plot as `bar_plot.png`. 4. **Violin Plot**: - Create a violin plot to show the distribution of `total_bill` for each day of the week. - Split the violins to show the distributions for males and females separately. - Save the plot as `violin_plot.png`. 5. **Faceted Plot**: - Create a faceted plot (using `catplot`) to show the distribution of `total_bill` across days of the week, separating the plots by time of day (`Lunch`, `Dinner`). - Use a swarm plot to display the data points. - Save the plot as `faceted_plot.png`. **Additional Instructions**: - Ensure that the axes and titles are appropriately labeled for all plots. - Use a consistent theme for all your plots. # Example Output Expectations for each of your plots:      # Constraints - Assume that seaborn and other necessary libraries are already installed. - The dataset `tips` can be loaded directly using `sns.load_dataset("tips")`. # Input and Output Formats - **Input**: Your script should not take any input from the user. It should load the dataset and generate the plots. - **Output**: Save the generated plots as images with filenames specified above in the current directory. ```python import seaborn as sns import matplotlib.pyplot as plt # Load the tips dataset tips = sns.load_dataset("tips") # Scatter plot scatter_plot = sns.catplot(data=tips, x="day", y="total_bill", hue="smoker", kind="swarm") scatter_plot.set_axis_labels("Day of the Week", "Total Bill") plt.title("Total Bill by Day of the Week (Colored by Smoker)") scatter_plot.savefig("scatter_plot.png") # Box plot box_plot = sns.catplot(data=tips, x="day", y="total_bill", hue="time", kind="box") box_plot.set_axis_labels("Day of the Week", "Total Bill") plt.title("Total Bill Distribution by Day of the Week (Colored by Time of Day)") box_plot.savefig("box_plot.png") # Bar plot bar_plot = sns.catplot(data=tips, x="day", y="tip", kind="bar", ci=95) bar_plot.set_axis_labels("Day of the Week", "Average Tip") plt.title("Average Tip by Day of the Week") bar_plot.savefig("bar_plot.png") # Violin plot violin_plot = sns.catplot(data=tips, x="day", y="total_bill", hue="sex", kind="violin", split=True) violin_plot.set_axis_labels("Day of the Week", "Total Bill") plt.title("Total Bill Distribution by Day of the Week (Split by Sex)") violin_plot.savefig("violin_plot.png") # Faceted plot faceted_plot = sns.catplot(data=tips, x="day", y="total_bill", hue="smoker", kind="swarm", col="time", aspect=0.7) faceted_plot.set_axis_labels("Day of the Week", "Total Bill") plt.subplots_adjust(top=0.9) faceted_plot.fig.suptitle("Total Bill by Day of the Week (Separated by Time of Day and Colored by Smoker)") faceted_plot.savefig("faceted_plot.png") ```