}];

  let description = [{
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
  }];

  let arguments = (ins

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

    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.

		{"Khojki", Var, 4},
		{"Khudawadi", Var, 4},
		{"L", Var, 0},
		{"Lao", Var, 0},
		{"Latin", Var, 0},
		{"Lepcha", Var, 0},
		{"Letter", Var, 0},
		{"Limbu", Var, 0},
		{"Linear_A", Var, 4},
		{"Linear_B", Var, 0},
		{"Lisu", Var, 0},
		{"Ll", Var, 0},
		{"Lm", Var, 0},
		{"Lo", Var, 0},
		{"Logical_Order_Exception", Var, 0},
		{"Lower", Var, 0},
		{"LowerCase", Const, 0},