r&f--nx?t--nx??ublk--nx???ppol?q&0-t&baol--nx?soum--nx?veib--nx??x-&ipphl--nx?r&embh--nx?imph--nx???y-tinks--nx??r&f-atsr--nx?g-&an&ms--nx?nd--nx??e&drf--nx?ngs--nx??murs--nx?netl--nx?olmb--nx?sorr--nx??h-&a&lms--nx?yrf--nx??emjt--nx??i&-&lboh--nx?rsir--nx?y&d&ar--nx?na--nx??ksa--nx?lem--nx?r&ul--nx?yd--nx????stu??j-&drav--nx?rolf--nx?sdav--nx??kua?l-&drojf--nx?lares--nx??m-tlohr--nx?n-esans--nx?olf?p-sdnil--nx?s-ladrl--nx?tih?v-rvsyt--nx??s&a&ns?ons??i&ar?er&dron?r&os?øs???ár??la&g?h??mor!t??si...

expression:
</p>

<pre>
var a, b, c = f() + v(), g(), sqr(u()) + v()

func f() int        { return c }
func g() int        { return a }
func sqr(x int) int { return x*x }

// functions u and v are independent of all other variables and functions
</pre>

<p>
The function calls happen in the order
<code>u()</code>, <code>sqr()</code>, <code>v()</code>,

  );

  let results = (outs
    TFL_TensorOf<[F32, I32, I64, QI16, QUI8, TFL_Quint8]>:$output
  );

  let hasOptions = 1;
}

def TFL_RsqrtOp: TFL_Op<"rsqrt", [Pure,
                                  QuantizableResult,
                                  TFL_SameFirstOperandAndFirstResultElementType,
                                  SameOperandsAndResultShape]> {

  }];
}

def TF_RsqrtGradOp : TF_Op<"RsqrtGrad", [Pure, TF_SameOperandsAndResultTypeResolveRef]> {
  let summary = "Computes the gradient for the rsqrt of `x` wrt its input.";

  let description = [{
Specifically, `grad = dy * -0.5 * y^3`, where `y = rsqrt(x)`, and `dy`
is the corresponding input gradient.
  }];

  let arguments = (ins
    TF_FpOrComplexTensor:$y,
    TF_FpOrComplexTensor:$dy

        non_feature_dims.push_back(i);
      }
      auto reduce_dims = GetI64ElementsAttr(non_feature_dims, &rewriter);
      auto scalar_broadcast_dims = rewriter.getDenseI64ArrayAttr({});

      // scratch1 = rsqrt(var + epsilon)
      RankedTensorType scalar_float =
          tensorflow::GetTypeFromTFTensorShape({}, kernel_type);
      auto epsilon = rewriter.create<ConstantOp>(

rb0QGj8yia+WA+If6hatbZmCg87CtDVpzpPIZUzX4UO+DRQEhezflt9uZ4YrFoYq
PmbL7znqm7/3kpenKrNoinGnzFG7wnOrDfMrBrZgL0Vt95zvs/PBTyE2r4GWCNms
PH6n50oTck5Lif0EtnIp26o9+vgkVVAFcHLUDy2y90g67j137SNnYu/47FD/rS8R
A2Y8wRd6lXXxI/n5Wyj+P0Vai5zq6BIFPMbpABEBAAE=
=IBHk
-----END PGP PUBLIC KEY BLOCK-----

pub    3F36885C24DF4B75
uid    Gregory Brail <******@****.***>

-----BEGIN PGP PUBLIC KEY BLOCK-----

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�������o�����1�����X�rccR�e�h/$�s^...

       *    `TfLiteSignatureRunnerResizeInputTensor`

    * New C API function `TfLiteExtensionApisVersion` added to `tensorflow/lite/c/c_api.h`.

    * Add int8 and int16x8 support for RSQRT operator

* Android NDK r25 is supported.

### Bug Fixes and Other Changes

*   Add TensorFlow Quantizer to TensorFlow pip package.

`);w.push(`  Use \`${i.replace("[",`[${_}:`)}\` for \`${E.trim()}\``);break}F.warn([`The class \`${i}\` is ambiguous and matches multiple utilities.`,...w,`If this is content and not a class, replace it with \`${i.replace("[","[").replace("]","]")}\` to silence this warning.`]);continue}}o=o.map(p=>p.filter(m=>Cd(m[1])))}o=o.flat(),o=Array.from(mC(o,r)),o=aC(o,e),a&&(o=oC(o,r));for(let p of n)o=lC(p,o,e);for(let p of o)p[1].raws.tailwind={...p[1].raws.tailwind,candidate:i},p=gC(p,{contex...

    "SLICE",
    "SIN",
    "TRANSPOSE_CONV",
    "SPARSE_TO_DENSE",
    "TILE",
    "EXPAND_DIMS",
    "EQUAL",
    "NOT_EQUAL",
    "LOG",
    "SUM",
    "SQRT",
    "RSQRT",
    "SHAPE",
    "POW",
    "ARG_MIN",
    "FAKE_QUANT",
    "REDUCE_PROD",
    "REDUCE_MAX",
    "PACK",
    "LOGICAL_OR",
    "ONE_HOT",
    "LOGICAL_AND",
    "LOGICAL_NOT",