^bb0(%arg0: tensor<? x f32>):
  // CHECK: "tfl.rsqrt"(%arg0)
  %0 = "tfl.rsqrt"(%arg0): (tensor<? x f32>) -> tensor<? x f32>
  func.return %0 : tensor<? x f32>
}

// CHECK-LABEL: testRsqrtQuant
func.func @testRsqrtQuant(%arg0: tensor<1x80x1x!quant.uniform<i8:f32, 0.048358432948589325:-128>>) -> tensor<1x80x1x!quant.uniform<i8:f32, 0.0066055487841367722:-128>> {

  explicit FusedBatchNormV3Pat(::mlir::MLIRContext *context)
      : ::mlir::RewritePattern(
            "tf.FusedBatchNormV3", 1, context,
            {"tf.Add", "tf.Const", "tf.Mul", "tf.Rsqrt", "tf.Sub"}) {}

  ::mlir::LogicalResult matchAndRewrite(
      ::mlir::Operation *fused_batch_norm,
      ::mlir::PatternRewriter &rewriter) const override {

            "Floor", "IsFinite", "IsInf", "IsNan", "Inv", "Reciprocal", "Log",
            "Log1p", "Invert", "LogicalNot", "Ndtri", "Neg", "Rint", "Round",
            "Rsqrt", "Sigmoid", "Sign", "Sinh", "Softplus", "Softsign", "Sqrt",
            "Square", "Tan", "Tanh", "Real", "Imag", "Erf", "Erfc", "Erfinv",
            "Lgamma", "Digamma",
            // Binary

  %cst = arith.constant dense<[0]> : tensor<1xi32>
  %0 = "tfl.square"(%arg0) : (tensor<2xf32>) -> tensor<2xf32>
  %1 = "tfl.sum"(%0, %cst) {keep_dims = false} : (tensor<2xf32>, tensor<1xi32>) -> tensor<f32>
  %2 = "tfl.rsqrt"(%1) : (tensor<f32>) -> tensor<f32>
  %3 = "tfl.mul"(%arg0, %2) {fused_activation_function = "NONE"} : (tensor<2xf32>, tensor<f32>) -> tensor<2xf32>
  func.return %3: tensor<2xf32>

  );

  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>(

       *    `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.

    "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",