list index out of range about face_recognition HOT 4 CLOSED

known_image = face_recognition.load_image_file("face_2.jpg")

# Since the image is already cropped, we know the face location is just the whole image size
face_location = (0, known_image.shape[1], known_image.shape[0], 0)

# Pass in an array of known face locations to skip the step of face detection
known_encoding = face_recognition.face_encodings(known_image, [face_location])[0]

print(known_encoding)

array([-0.07084331,  0.13144939,  0.06481101, -0.05566324, -0.10227886,
        0.02378505, -0.02929443, -0.16121498,  0.09502754, -0.09116761,
        0.16282618,  0.01120954, -0.22670874, -0.07019024,  0.07189877,
        0.08362474, -0.13715625, -0.04159784, -0.13862796, -0.04521821,
        0.06266126,  0.09748268,  0.06780834, -0.03782643, -0.09195558,
       -0.32051048, -0.06524452, -0.07020397,  0.02561777, -0.06362872,
        0.02519293, -0.03309638, -0.25325459, -0.05337401, -0.04081663,
        0.02959361, -0.1108077 , -0.10953825,  0.20097654, -0.03071829,
       -0.19747683,  0.10650204,  0.05199543,  0.17633158,  0.20601764,
       -0.08934571, -0.00882005, -0.07672026,  0.15298177, -0.20318791,
        0.06232864,  0.14546208,  0.10586035,  0.09767602,  0.07987885,
       -0.03630037,  0.09194864,  0.12821943, -0.18550503,  0.01531077,
        0.16177645, -0.11098912, -0.0145441 , -0.00749754,  0.13307199,
        0.06760155, -0.06180105, -0.12176102,  0.0763557 , -0.10216746,
       -0.0853331 ,  0.01694381, -0.12842602, -0.12923945, -0.33743614,
        0.06042334,  0.28734946,  0.14279479, -0.21950229, -0.00438824,
       -0.12347218,  0.02389126,  0.0859604 ,  0.06279413, -0.01622978,
       -0.06947765, -0.07259755,  0.09392088,  0.17706268, -0.02612238,
       -0.02010851,  0.31076789,  0.10003579, -0.02693687,  0.03024578,
        0.05860615, -0.10163979, -0.08813927, -0.1216615 ,  0.03181211,
        0.0669135 , -0.1004982 ,  0.00465229,  0.19129516, -0.24604803,
        0.23892471, -0.00085283, -0.00195196,  0.07778236, -0.06403588,
       -0.03284847, -0.00111775,  0.07869174, -0.20308636,  0.21220191,
        0.12809221, -0.01532205,  0.09983703,  0.0276077 ,  0.06673215,
       -0.07806585,  0.00037921, -0.20924436, -0.12015466,  0.05508876,
        0.06190829,  0.0230726 ,  0.07592089])