int vectorSize = 3;
  Tensor tags(DT_STRING, {vectorSize});
  Tensor values(DT_FLOAT, {vectorSize});
  tags.vec<tstring>()(0) = "tag1";
  tags.vec<tstring>()(1) = "tag2";
  tags.vec<tstring>()(2) = "tag3";
  values.vec<float>()(0) = 1.0f;
  values.vec<float>()(1) = -0.73f;
  values.vec<float>()(2) = 10000.0f;
  TestScalarSummaryOp(&tags, &values, R"(
                      value { tag: 'tag1' simple_value: 1.0 }

  auto type = elements.getType();
  auto elemType = type.getElementType();
  if (elemType.isSignlessInteger(1)) {
    auto vec = llvm::to_vector(
        llvm::map_range(elements.getValues<bool>(),
                        [&](bool value) -> uint8_t { return value ? 1 : 0; }));
    return std::vector<bool>(vec.begin(), vec.end());
  }

  return std::vector<bool>();
}

template <>

                                    StringRef(content.data(), content.size()));
  }

  std::vector<int64_t> FlatbufferVecToMlirVec(const flexbuffers::Vector& vec) {
    std::vector<int64_t> temp(vec.size(), 0);
    for (int i = 0; i < vec.size(); i++) {
      temp[i] = vec[i].AsInt64();
    }
    return temp;
  }

  llvm::SmallVector<mlir::NamedAttribute, 4> ReadAttr(const flexbuffers::Map& m,

		8382142935188824023, 9103922860780351547, 4152330101494654406,
	}
)

type rngSource struct {
	tap  int           // index into vec
	feed int           // index into vec
	vec  [rngLen]int64 // current feedback register
}

// seed rng x[n+1] = 48271 * x[n] mod (2**31 - 1)
func seedrand(x int32) int32 {
	const (
		A = 48271
		Q = 44488
		R = 3399
	)

func RatioedGaugeVecPhasedElementPair(vec RatioedGaugeVec, initialWaitingDenominator, initialExecutingDenominator float64, labelValues []string) RatioedGaugePair {
	return RatioedGaugePair{
		RequestsWaiting:   vec.NewForLabelValuesSafe(0, initialWaitingDenominator, append([]string{LabelValueWaiting}, labelValues...)),

	buckets := []float64{0, 1}
	vec := NewTestableTimingRatioHistogramVec(clk.Now,
		&compbasemetrics.TimingHistogramOpts{Name: testHistogramName, Buckets: buckets},
		"alabel",
	)
	toRegister := vec.metrics()
	registry := compbasemetrics.NewKubeRegistry()
	for _, reg := range toRegister {
		registry.MustRegister(reg)
	}
	tro, err := vec.NewForLabelValuesChecked(0, 1, []string{"avalue"})
	if err != nil {

// Use of this source code is governed by a BSD-style
// license that can be found in the LICENSE file.

package main

import (
	"fmt"
)

func Sum[T interface{ int | float64 }](vec []T) T {
	var sum T
	for _, elt := range vec {
		sum = sum + elt
	}
	return sum
}

func Abs(f float64) float64 {
	if f < 0.0 {
		return -f
	}
	return f
}

func main() {
	vec1 := []int{3, 4}

      }
    }
  }

 private:
  // TODO(b/174778737): Only hold needed outputs.
  vector<AbstractTensorHandle*> forward_outputs_;
};

Status BroadcastMul(AbstractContext* ctx, AbstractTensorHandle* vec,
                    AbstractTensorHandle* mat,
                    absl::Span<AbstractTensorHandle*> outputs) {
  if (!isa<ImmediateExecutionContext>(ctx)) {
    // TODO(b/168850692): Fix this.

            ASN1Sequence vec = (ASN1Sequence) constructed.getBaseObject();


                ASN1ObjectIdentifier spnego = (ASN1ObjectIdentifier) vec.getObjectAt(0);
                if ( !SPNEGO_OID.equals(spnego) ) {
                    throw new IOException("Malformed SPNEGO token, OID " + spnego);
                }
                ASN1TaggedObject tagged = (ASN1TaggedObject) vec.getObjectAt(1);

  return false;
}

// Multiply after broadcasting vec to match dimensions of mat.
//   Args:
//     vec: A 1-D tensor of dimension [D0]
//     mat: A 2-D tensor of dimension [D0, D1]
//
//   Returns:
//     A tensor of dimension [D0, D1], the result for vec * mat.
Output BroadcastMul(const Scope& scope, const Output& vec, const Output& mat) {
  auto reshaped = ExpandDims(scope, vec, -1);
  return Multiply(scope, reshaped, mat);
}