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Manu · Accepted Answer

Step 1: Mutable Default Argument The default argument metrics=[] is mutable. If it's modified, the modification persists across function calls. Explanation: We will initialize metrics inside the function if it's not provided. This avoids the issue with the mutable default argument. Step 2: Correcting the Lambda Functions The lambda functions are appended inside a loop and are supposed to capture the loop variable i . However, due to Python's late binding behavior, all lambdas capture the same final value of i . Corrected Code Here's the modified code with explanations: def get_sum_metrics(predictions, metrics=None): if metrics is None: metrics = [] # Initialize metrics if not provided for i in range(3): # Pass `i` as a default argument to lambda to capture the current value during each iteration metrics.append(lambda x, i=i: x + i) sum_metrics = 0 for metric in metrics: sum_metrics += metric(predictions) # Apply each metric to predictions and sum the results return sum_metrics # Test the function with a prediction value print(get_sum_metrics(10)) # Expected output: 10 + 0 + 10 + 1 + 10 + 2 = 33 Explanation:  To correct this, we'll pass  i  as a default argument to each lambda. This captures the current value of  i  during each iteration of the loop. Output Initialization of metrics : We initialize metrics inside the function to avoid using a mutable default argument. This ensures that metrics is a new list each time the function is called unless an explicit list is provided. Modification to the Lambda Function : We add i=i in the lambda function definition. This syntax sets i as a default parameter to the lambda function. Default parameters in Python are evaluated at the time of function definition, not at the time of execution. This ensures each lambda captures its corresponding i correctly. Final Answer: The get_sum_metrics function was corrected to handle mutable default arguments safely and to ensure proper capturing of loop variables in lambda functions. By initializing metrics inside the function and using i=i in lambda definitions, each metric now accurately reflects its intended calculation. These adjustments ensure the function performs reliably and as expected across different test conditions.

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