Correct Python code

Project Title: Correct Python code Project Description: I am seeking a Python developer to help me correct a syntax error in my code. Skills and Experience Required: - Strong knowledge and experience in Python programming - Proficiency in identifying and fixing syntax errors - Intermediate level understanding of Python code complexity Requirements: - Ability to analyze and debug code to identify the specific syntax error - Experience in effectively correcting syntax errors in Python code - Familiarity with intermediate level Python code complexity - Attention to detail and ability to ensure the corrected code runs smoothly Additional Information: - The corrected code should include both comments and documentation to ensure clarity and understanding. - The developer should provide explanations and comments throughout the code to facilitate future maintenance and updates. Code is below its designed to determine spares requirements using confidence level. from [login to view URL] import norm def poisson_distribution(lambda_value, k): """ This function calculates the probability of k occurrences for a Poisson distribution with parameter lambda. Args: lambda_value: The average number of occurrences. k: The number of occurrences. Returns: The probability of k occurrences. """ return [login to view URL](lambda_value, k) * [login to view URL](-lambda_value) / [login to view URL](k) def calculate_required_spares(average_failures_per_period: float, desired_confidence_level: float, lead_time_in_periods: int) -> int: """ Calculates the required number of spares based on the Poisson distribution and desired confidence level. Args: average_failures_per_period: The average number of failures per period. desired_confidence_level: The desired confidence level (between 0 and 1). lead_time_in_periods: The lead time in periods. Returns: The required number of spares. """ if desired_confidence_level < 0 or desired_confidence_level > 1: raise ValueError("Invalid confidence level. Please enter a value between 0 and 1.") average_failures_lead_time = average_failures_per_period * lead_time_in_periods z_score = [login to view URL](2 * desired_confidence_level) return int([login to view URL](average_failures_lead_time + z_score * [login to view URL](average_failures_lead_time))) # Get user input average_failures_per_period = float(input("Enter the average number of failures per period: ")) desired_confidence_level = float(input("Enter the desired confidence level (0-1): ")) lead_time_in_periods = int(input("Enter the lead time (in periods): ")) # Calculate required number of spares required_number_of_spares = calculate_required_spares(average_failures_per_period, desired_confidence_level, lead_time_in_periods) # Print results print(f"The required number of spares is: {required_number_of_spares}") # Optionally, calculate probability of having insufficient spares probability_insufficient = 1 - sum([poisson_distribution(average_failures_lead_time, k) for k in range(required_number_of_spares)]) if probability_insufficient: print(f"The probability of having insufficient spares is: {probability_insufficient:.4f}")