Status RunOnce(const RunOptions& run_options,
               const std::vector<std::pair<string, Tensor>>& inputs,
               const std::vector<string>& output_tensor_names,
               const std::vector<string>& target_node_names,
               std::vector<Tensor>* outputs, RunMetadata* run_metadata,
               Session* session) {
  CallableOptions callable_options;
  std::vector<Tensor> feed_tensors;

REGISTER_NO_GRADIENT_OP("NonMaxSuppressionV5");

Status ResizeNearestNeighborGradHelper(const Scope& scope, const Operation& op,
                                       const std::vector<Output>& grad_inputs,
                                       std::vector<Output>* grad_outputs) {
  bool align_corners;
  TF_RETURN_IF_ERROR(
      GetNodeAttr(op.node()->attrs(), "align_corners", &align_corners));
  bool half_pixel_centers;

// license that can be found in the LICENSE file.

// Tests whether the non vector routines are working, even when the tests are run on a
// vector-capable machine.
package math_test

import (
	. "math"
	"testing"
)

func TestCosNovec(t *testing.T) {
	if !HasVX {
		t.Skipf("no vector support")
	}
	for i := 0; i < len(vf); i++ {
		if f := CosNoVec(vf[i]); !veryclose(cos[i], f) {

  Status Shape(const std::vector<int64_t>** shape) const;
  TF_DataType dtype() const { return dtype_; }

  // Sets its output argument to a summary of the values of this tensor on every
  // component device.
  Status SummarizeValue(std::string& summary);

  std::vector<TensorHandlePtr> release_tensors() { return std::move(tensors_); }

  std::vector<TFE_TensorHandle*> tensors() const {

Status PreparePjRtExecutableArguments(
    int num_missing_prefix_ctx_inputs, const std::vector<int>& input_mapping,
    const std::vector<const Tensor*>& inputs,
    const absl::flat_hash_map<int, const Tensor*>& variable_snapshots,
    xla::PjRtClient* pjrt_client, xla::PjRtDevice* pjrt_device,
    bool use_pjrt_tensor_buffer, std::vector<xla::PjRtBuffer*>* args,
    std::vector<std::unique_ptr<xla::PjRtBuffer>>* owned_args,

{"Name":"ADD (vector)","Bits":"0|1|0|1|1|1|1|0|size:2|1|Rm:5|1|0|0|0|0|1|Rn:5|Rd:5","Arch":"Scalar Scalar variant","Syntax":"ADD <V><d>, <V><n>, <V><m>","Code":"","Alias":""},

      {"TFL_Quint8", "TFLite quint8 type"},
  });

  return *entries;
}

void EmitDynamicRangeOp(std::vector<Record *> &defs, raw_ostream *ostream) {
  std::string dynamic_quant_kernel_support_regex =
      "bool GetDynamicRangeQuantKernelSupport() { return true; }";
  raw_ostream &os = *ostream;
  std::vector<std::string> weight_only;
  llvm::sort(defs, LessRecord());

#include <vector>

#include "tensorflow/c/experimental/saved_model/public/concrete_function_list.h"
#include "tensorflow/cc/saved_model/experimental/public/concrete_function.h"

namespace tensorflow {
namespace experimental {
namespace cc {

// ConcreteFunctionList helps convert an opaque pointer to an array of
// ConcreteFunction pointers to a std::vector.
class ConcreteFunctionList {
 public:

                std::vector<Tensor>* out) {
  ClientSession session(scope);
  TF_CHECK_OK(session.Run(tensors, out));
}

void GetTensor(const Scope& scope, Output tensor, Tensor* out) {
  std::vector<Tensor> outputs;
  GetTensors(scope, {std::move(tensor)}, &outputs);
  *out = outputs[0];
}

void GetTensors(const Scope& scope, const std::vector<Output>& assign_vars,

  SignatureDefFunctionMetadata() = default;
  SignatureDefFunctionMetadata(std::vector<SignatureDefParam> arguments,
                               std::vector<SignatureDefParam> returns);

  const std::vector<SignatureDefParam>& arguments() const;
  const std::vector<SignatureDefParam>& returns() const;

 private:
  std::vector<SignatureDefParam> arguments_;
  std::vector<SignatureDefParam> returns_;
};

}  // namespace tensorflow