auto shape = GetAsVector(quant_tensor->shape());
        if (kUseUpdatedHybridSchemeDefault) {
          EXPECT_EQ(quant_tensor->quantization()->scale()->size(), shape[0]);
        } else {
          EXPECT_EQ(quant_tensor->quantization()->scale()->size(), 1);
        }
      } else {
        EXPECT_EQ(quant_tensor->type(), TensorType_FLOAT32);
      }
    }

        if (op->hasOneUse() &&
            op->user_begin()->hasTrait<OpTrait::IsTerminator>())
          return failure();
      }
      // If the quantize op is a requantize op, it is being used in other scale
      // adjustments and should be kept. Instead, moving dequantize op before
      // the requantize op to remove the unnecessary requantize op.
      if (auto qtype =

    if (auto q = llvm::dyn_cast_or_null<quantfork::QuantizeCastOp>(input_op)) {
      if (!q->getAttr(kVolatileOpAttrName)) return failure();

      // If the quantize op is a requantize op, it is being used in other scale
      // adjustments and should be kept. Instead, move dequantize op before the
      // requantize op to remove the unnecessary requantize op.
      if (const QuantizedType qtype =

          input_tensor: Input tensor to perform convolution on.

        Returns:
          A map of: output key -> output result.
        """
        scale = [1.0] * self.out_channel_size
        offset = [0.5] * self.out_channel_size
        mean, variance = scale, offset
        out = nn_ops.depthwise_conv2d_native(
            input_tensor,
            self.filters,
            strides=[1, 2, 2, 1],

		argLen:         2,
		hasSideEffects: true,
		generic:        true,
	},
}

func (o Op) Asm() obj.As          { return opcodeTable[o].asm }
func (o Op) Scale() int16         { return int16(opcodeTable[o].scale) }
func (o Op) String() string       { return opcodeTable[o].name }
func (o Op) SymEffect() SymEffect { return opcodeTable[o].symEffect }

var xxx_messageInfo_RollingUpdateStatefulSetStrategy proto.InternalMessageInfo

func (m *Scale) Reset()      { *m = Scale{} }
func (*Scale) ProtoMessage() {}
func (*Scale) Descriptor() ([]byte, []int) {
	return fileDescriptor_2747f709ac7c95e7, []int{12}
}
func (m *Scale) XXX_Unmarshal(b []byte) error {
	return m.Unmarshal(b)
}
func (m *Scale) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) {
	b = b[:cap(b)]

		a.writeNameTo(w, abiDetail)

	case TYPE_MEM:
		a.WriteNameTo(w)
		if a.Index != REG_NONE {
			if a.Scale == 0 {
				// arm64 shifted or extended register offset, scale = 0.
				fmt.Fprintf(w, "(%v)", Rconv(int(a.Index)))
			} else {
				fmt.Fprintf(w, "(%v*%d)", Rconv(int(a.Index)), int(a.Scale))
			}
		}

	case TYPE_CONST:
		io.WriteString(w, "$")
		a.WriteNameTo(w)
		if a.Reg != 0 {

            "type": "string"
          },
          "replicas": {

}

TFE_MonitoringBuckets* TFE_MonitoringNewExponentialBuckets(double scale,
                                                           double growth_factor,
                                                           int bucket_count) {
  return new TFE_MonitoringBuckets([scale, growth_factor, bucket_count]() {
    return tensorflow::monitoring::Buckets::Exponential(scale, growth_factor,

//              scale * rsqrt(variance + epsilon)
// CHECK:  %[[MUL1:.*]] = "tf.Mul"(%[[ARG1:.*]], %[[RSQRT]])
//              x * scale * rsqrt(variance + epsilon)
// CHECK:  %[[MUL2:.*]] = "tf.Mul"(%[[ARG0:.*]], %[[MUL1]])
//              mean * scale * rsqrt(variance + epsilon)
// CHECK:  %[[MUL3:.*]] = "tf.Mul"(%[[ARG3:.*]], %[[MUL1]])
//              offset - mean * scale * rsqrt(variance + epsilon)