layers in order to work, where the Keras symbolic shape inference proves
        insufficient.
    *   Code that tries manually walking a `tf.keras.Model` layer by layer and
        assumes layers only ever have one positional argument. This assumption
        doesn't hold true before TF 2.4 either, but is more likely to cause
        issues now.
    *   Code that manually enters `keras.backend.get_graph()` before building a

2105          ; disallowed_STD3_mapped ; 0063 002F 006F #1.1  CARE OF
2106          ; disallowed_STD3_mapped ; 0063 002F 0075 #1.1  CADA UNA
2107          ; mapped                 ; 025B          # 1.1  EULER CONSTANT
2108          ; valid                  ;      ; NV8    # 1.1  SCRUPLE
2109          ; mapped                 ; 00B0 0066     # 1.1  DEGREE FAHRENHEIT

bits; excess != 0 { b[0] >>= excess } // Don't let the value be too small: set the most significant two bits. // Setting the top two bits, rather than just the top bit, means that // when two of these values are multiplied together, the result isn't // ever one bit short. if excess := len(b)*8 - bits; excess < 7 { b[0] |= 0b1100_0000 >> excess } else { b[0] |= 0b0000_0001 b[1] |= 0b1000_0000 } // Make the value odd since an even number certainly isn't prime. b[len(b)-1] |= 1 // We don't need to check...