of other integer types) in `tf.constant`.
*   Make the `gain` argument of convolutional orthogonal initializers
    (`convolutional_delta_orthogonal`, `convolutional_orthogonal_1D`,
    `convolutional_orthogonal_2D`, `convolutional_orthogonal_3D`) have
    consistent behavior with the `tf.initializers.orthogonal` initializer, i.e.
    scale the output l2-norm by `gain` and NOT by `sqrt(gain)`. (Note that these

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FD76..FD77    ; mapped                 ; 0639 0645 0645 #1.1  ARABIC LIGATURE AIN WITH MEEM WITH MEEM FINAL FORM..ARABIC LIGATURE AIN WITH MEEM WITH MEEM INITIAL FORM
FD78          ; mapped                 ; 0639 0645 0649 #1.1  ARABIC LIGATURE AIN WITH MEEM WITH ALEF MAKSURA FINAL FORM

Rain-bow._

[Illustration: FIG. 14.]

This Bow never appears, but where it rains in the Sun-shine, and may be
made artificially by spouting up Water which may break aloft, and
scatter into Drops, and fall down like Rain. For the Sun shining upon
these Drops certainly causes the Bow to appear to a Spectator standing
in a due Position to the Rain and Sun. And hence it is now agreed upon,

it downstream (when targeting a different environment, like TensorFlow lite for
example).
The MLIR module is expected to have a main() function that will be used as an
entry point. The inputs to the operations will be passed as argument to the
main() function and the returned values of the main function mapped to the
outputs.
Example usage:

```
import tensorflow as tf

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