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  }];

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That is for rows we have grad for, we update var, accum and linear as follows:
accum_new = accum + grad * grad
linear += grad - (accum_new^(-lr_power) - accum^(-lr_power)) / lr * var
quadratic = 1.0 / (accum_new^(lr_power) * lr) + 2 * l2
var = (sign(linear) * l1 - linear) / quadratic if |linear| > l1 else 0.0
accum = accum_new
  }];

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1003B         ; disallowed                             # NA   <reserved-1003B>
1003C..1003D  ; valid                                  # 4.0  LINEAR B SYLLABLE B017 ZA..LINEAR B SYLLABLE B074 ZE
1003E         ; disallowed                             # NA   <reserved-1003E>
1003F..1004D  ; valid                                  # 4.0  LINEAR B SYLLABLE B020 ZO..LINEAR B SYLLABLE B091 TWO

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    It significantly reduces the peak memory required and latency incurred
    during restore.
*   Added a new library for library of matrix-free (iterative) solvers for
    linear equations, linear least-squares, eigenvalues and singular values in
    tensorflow/contrib/solvers. Initial version has lanczos bidiagonalization,
    conjugate gradients and CGLS.

dilated and spread by the Refraction of the first Prism, so that they
should not any longer go in single Lines to single Points, but that
every Ray being split, shattered, and changed from a Linear Ray to a
Superficies of Rays diverging from the Point of Refraction, and lying in
the Plane of the Angles of Incidence and Refraction, they should go in
those Planes to so many Lines reaching almost from one end of the Image