Contrast is adjusted independently for each channel of each image.

For each channel, the Op first computes the mean of the image pixels in the
channel and then adjusts each component of each pixel to
`(x - mean) * contrast_factor + mean`.
  }];

  let arguments = (ins
    Arg<TensorOf<[TF_Float16, TF_Float32]>, [{Images to adjust.  At least 3-D.}]>:$images,

        mean, var = tf.nn.moments(self.kernel, axes=[0, 1, 2], keepdims=True)
        return self.convolution_op(inputs, (self.kernel - mean) / tf.sqrt(var +
        1e-10))` Alternatively, you can override `convolution_op`: `python class
        StandardizedConv2D(tf.keras.Layer): def convolution_op(self, inputs,
        kernel): mean, var = tf.nn.moments(kernel, axes=[0, 1, 2],

                        All valid rules within a Route attached to this Listener should be
                        implemented. Invalid Route rules can be ignored (sometimes that will mean
                        the full Route). If a Route rule transitions from valid to invalid,
                        support for that Route rule should be dropped to ensure consistency. For

1F3A0..1F3C4  ; valid                  ;      ; NV8    # 6.0  CAROUSEL HORSE..SURFER
1F3C5         ; valid                  ;      ; NV8    # 7.0  SPORTS MEDAL
1F3C6..1F3CA  ; valid                  ;      ; NV8    # 6.0  TROPHY..SWIMMER
1F3CB..1F3CE  ; valid                  ;      ; NV8    # 7.0  WEIGHT LIFTER..RACING CAR