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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def call_mr_transform(data, opt='', path='./', remove_files=True):
 # pragma: no cover r"""Call mr_transform This method calls the iSAP module mr_transform Parameters data : np.ndarray Input data, 2D array opt : list or str, optional Options to be passed to mr_transform path : str, optional Path for output files (default is './') remove_files : bool, optional Option to remove output files (default is 'True') Returns ------- np.ndarray results of mr_transform Raises ------ ValueError If the input data is not a 2D numpy array Examples -------- array([[[-1.5 , -1.125 , -0.75 ], [-0.375 , 0. , 0.375 ], [ 0.75 , 1.125 , 1.5 ]], [[-1.5625 , -1.171875 , -0.78125 ], [-0.390625 , 0. , 0.390625 ], [ 0.78125 , 1.171875 , 1.5625 ]], [[-0.5859375 , -0.43945312, -0.29296875], [-0.14648438, 0. , 0.14648438], [ 0.29296875, 0.43945312, 0.5859375 ]], [[ 3.6484375 , 3.73632812, 3.82421875], [ 3.91210938, 4. , 4.08789062], [ 4.17578125, 4.26367188, 4.3515625 ]]], dtype=float32) """


    if not import_astropy:
        raise ImportError('Astropy package not found.')

    if (not isinstance(data, np.ndarray)) or (data.ndim != 2):
        raise ValueError('Input data must be a 2D numpy array.')

    executable = 'mr_transform'

    # Make sure mr_transform is installed.
    is_executable(executable)

    # Create a unique string using the current date and time.
    unique_string = datetime.now().strftime('%Y.%m.%d_%H.%M.%S')

    # Set the ouput file names.
    file_name = path + 'mr_temp_' + unique_string
    file_fits = file_name + '.fits'
    file_mr = file_name + '.mr'

    # Write the input data to a fits file.
    fits.writeto(file_fits, data)

    if isinstance(opt, str):
        opt = opt.split()

    # Call mr_transform.
    try:

        check_call([executable] + opt + [file_fits, file_mr])

    except Exception:

        warn('{} failed to run with the options provided.'.format(executable))
        remove(file_fits)

    else:

        # Retrieve wavelet transformed data.
        result = fits.getdata(file_mr)

        # Remove the temporary files.
        if remove_files:
            remove(file_fits)
            remove(file_mr)

        # Return the mr_transform results.
        return result