"tooltip": false,
              "viz": false
            },
            "insertNulls": false,
            "lineInterpolation": "linear",
            "lineWidth": 1,
            "pointSize": 5,
            "scaleDistribution": {
              "type": "linear"
            },
            "showPoints": "never",
            "spanNulls": false,
            "stacking": {
              "group": "A",

xt","axisLabel":"","axisPlacement":"auto","barAlignment":0,"drawStyle":"line","fillOpacity":10,"gradientMode":"none","hideFrom":{"legend":false,"tooltip":false,"viz":false},"insertNulls":false,"lineInterpolation":"linear","lineWidth":1,"pointSize":5,"scaleDistribution":{"type":"linear"},"showPoints":"never","spanNulls":false,"stacking":{"group":"A","mode":"none"},"thresholdsStyle":{"mode":"off"}},"mappings":[],"thresholds":{"mode":"absolute","steps":[{"color":"green","value":null},{"color":"red"...

  }];

  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

    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.

}

def TFL_EluOp: TFL_Op<"elu", [
    Pure,
    SameOperandsAndResultShape,
    TFL_SameFirstOperandAndFirstResultElementType]> {
  let summary = "Exponential Linear Unit operator";
  let description = [{
    Computes the exponential linear
      f(x) -> exp(x) - 1 for x < 0, x for x >= 0.
    element-wise.
  }];

  let arguments = (ins TFL_TensorOf<[F32, I8]>:$x);