r:#ececec}.bg-gradient-purple.btn:hover{background:#5e37a6 linear-gradient(180deg,#7655b4,#5e37a6) repeat-x!important}.bg-gradient-purple.btn.active,.bg-gradient-purple.btn:active,.bg-gradient-purple.btn:not(:disabled):not(.disabled).active,.bg-gradient-purple.btn:not(:disabled):not(.disabled):active{border-color:#533093;color:#fff}.bg-gradient-purple.btn.active,.bg-gradient-purple.btn:active,.bg-gradient-purple.btn:not(:disabled):not(.disabled).active,.bg-gradient-purple.btn:not(:disabled):not(...

the outward edge of the Iris a red which on the outside inclined to
purple.

This Iris was immediately encompassed with a second, whose Colours were
in order from the inside outwards, purple, blue, green, yellow, light
red, a red mix'd with purple.

Then immediately follow'd the Colours of the third Iris, which were in
order outwards a green inclining to purple, a good green, and a red more
bright than that of the former Iris.

     n,iY,bY,iX,bX,oC

This operation is useful for resizing the activations between convolutions
(but keeping all data), e.g. instead of pooling. It is also useful for training
purely convolutional models.

For example, given an input of shape `[1, 1, 1, 4]`, data_format = "NHWC" and
block_size = 2:

```
x = [[[[1, 2, 3, 4]]]]

```

27B0          ; valid                  ;      ; NV8    # 6.0  CURLY LOOP
27B1..27BE    ; valid                  ;      ; NV8    # 1.1  NOTCHED UPPER RIGHT-SHADOWED WHITE RIGHTWARDS ARROW..OPEN-OUTLINED RIGHTWARDS ARROW
27BF          ; valid                  ;      ; NV8    # 6.0  DOUBLE CURLY LOOP
27C0..27C6    ; valid                  ;      ; NV8    # 4.1  THREE DIMENSIONAL ANGLE..RIGHT S-SHAPED BAG DELIMITER

$start-color, $end-color);\n  background-repeat: repeat-x;\n}\n@mixin gradient-x-three-colors($start-color: $blue, $mid-color: $purple, $color-stop: 50%, $end-color: $red) {\n  background-image: linear-gradient(to right, $start-color, $mid-color $color-stop, $end-color);\n  background-repeat: no-repeat;\n}\n@mixin gradient-y-three-colors($start-color: $blue, $mid-color: $purple, $color-stop: 50%, $end-color: $red) {\n  background-image: linear-gradient($start-color, $mid-color $color-stop, $end-color);\n...

      * If you use the very rarely-used function `tf.keras.mixed_precision.experimental.get_layer_policy`:
          * Replace `tf.keras.mixed_precision.experimental.get_layer_policy(layer)` with `layer.dtype_policy`.