test_data = np.random.rand(1, 3, 22, 22) test_filter = np.random.rand(3, 3, 3, 64)

Thank you for providing the complete code snippet.

The `computeMse()` function seems to be incomplete as it is missing the closing parenthesis for the `pow()` function. Assuming that you want to calculate the mean squared error between `data1` and `data2`, you can modify the function as follows:

import numpy as np

def computeMse(data1, data2):
    errors = []
    for i in range(len(data1)):
        errors.append(data1[i] - data2[i])

    squared_error = []
    for val in errors:
        squared_error.append(pow(val, 2))  # add closing parenthesis for pow()

    mse = np.mean(squared_error)
    return mse

test_data = np.random.rand(1, 3, 22, 22)
test_filter = np.random.rand(3, 3, 3, 64)

mse = computeMse(test_data, test_filter)
print("Mean Squared Error:", mse)

In this example, `test_data` and `test_filter` are randomly generated numpy arrays. The `computeMse()` function calculates the difference between corresponding elements of `data1` and `data2`, squares each difference, calculates the mean of the squared errors, and returns the mean squared error. Finally, the function is called with `test_data` and `test_filter` as inputs, and the resulting mean squared error is printed.