human
Solve the following python programming problem given the following code:
def load_gray(random_seed = 123522) : 
    data_path = os.path.join (os.path.split (__file__) [0], 'data') 
    if (not os.path.exists (data_path)) : 
        os.makedirs (data_path) 
dataset = 'train.zip' 
    data_file = os.path.join (data_path, dataset) 
    if os.path.isfile (data_file) : 
        dataset = data_file 
if (not os.path.isfile (data_file)) : 
        try : 
            import urllib 
            urllib.urlretrieve ('http://google.com') 
except AttributeError : 
            import urllib.request as urllib 
url = 'https://dl.dropboxusercontent.com/u/15378192/train.zip' 
        print (('Downloading data from %s' % url)) 
        urllib.urlretrieve (url, data_file) 
data_dir = os.path.join (data_path, 'cvd') 
    if (not os.path.exists (data_dir)) : 
        os.makedirs (data_dir) 
        zf = zipfile.ZipFile (data_file) 
        zf.extractall (data_dir) 
data_file = os.path.join (data_path, 'cvd_gray.npy') 
    label_file = os.path.join (data_path, 'cvd_gray_labels.npy') 
    if (not os.path.exists (data_file)) : 
        print ('... loading data') 
        cat_matches = [] 
        dog_matches = [] 
        for (root, dirname, filenames) in os.walk (data_dir) : 
            for filename in fnmatch.filter (filenames, 'cat*') : 
                cat_matches.append (os.path.join (root, filename)) 
for filename in fnmatch.filter (filenames, 'dog*') : 
                dog_matches.append (os.path.join (root, filename)) 
sort_key = (lambda x : int (x.split ('.') [(- 2)])) 
        cat_matches = sorted (cat_matches, key = sort_key) 
        dog_matches = sorted (dog_matches, key = sort_key) 
        def square_and_gray(X) : 
            gray_consts = np.array ([[0.299], [0.587], [0.144]]) 
            return imresize (X, (48, 48)).dot (gray_consts).squeeze () 
X_cat = np.asarray ([square_and_gray (mpimg.imread (f)) for f in cat_matches]) 
        y_cat = np.zeros ((len (square_and_gray),)) 
        X_dog = np.asarray ([square_and_gray (mpimg.imread (f)) for f in dog_matches]) 
        y_dog =...

    
    

    Fix the buggy line: y_cat = np.zeros ((len (square_and_gray),))
assistant
#Here is Python code to fix the buggy line: y_cat = np.

y_cat = np.zeros ((len (X_cat),))