ops     fileOps
			wantCnt int64
			wantErr error
		}
		testClose struct { // Close() == wantErr
			wantErr error
		}
		testFnc any // testHeader | testWrite | testReadFrom | testClose
	)

	vectors := []struct {
		file  string // Optional filename of expected output
		tests []testFnc
	}{{
		// The writer test file was produced with this command:
		// tar (GNU tar) 1.26
		//   ln -s small.txt link.txt

#include "tensorflow/core/platform/errors.h"
#include "tensorflow/core/platform/test.h"

namespace tensorflow {
namespace gradients {
namespace internal {
namespace {
using std::vector;
using tensorflow::TF_StatusPtr;
using tracing::TracingOperation;

class CppGradients
    : public ::testing::TestWithParam<std::tuple<const char*, bool, bool>> {
 protected:
  void SetUp() override {

        "analyzer": "standard_analyzer",
        "search_analyzer": "standard_search_analyzer",
        "term_vector": "with_positions_offsets"
      },
      "content": {
        "type": "text",
        "analyzer": "standard_analyzer",
        "search_analyzer": "standard_search_analyzer",
        "term_vector": "with_positions_offsets"
      },
      "content_minhash": {
        "type": "keyword",
        "index": false

        "analyzer": "standard_analyzer",
        "search_analyzer": "standard_search_analyzer",
        "term_vector": "with_positions_offsets"
      },
      "content": {
        "type": "text",
        "analyzer": "standard_analyzer",
        "search_analyzer": "standard_search_analyzer",
        "term_vector": "with_positions_offsets"
      },
      "content_minhash": {
        "type": "keyword",
        "index": false

        "analyzer": "standard_analyzer",
        "search_analyzer": "standard_search_analyzer",
        "term_vector": "with_positions_offsets"
      },
      "content": {
        "type": "text",
        "analyzer": "standard_analyzer",
        "search_analyzer": "standard_search_analyzer",
        "term_vector": "with_positions_offsets",
        "copy_to": ["content_minhash_bits"]
      },
      "content_minhash": {

limitations under the License.
==============================================================================*/
#ifndef TENSORFLOW_C_EAGER_C_API_TEST_UTIL_H_
#define TENSORFLOW_C_EAGER_C_API_TEST_UTIL_H_

#include <vector>

#include "tensorflow/c/eager/c_api.h"
#include "tensorflow/c/eager/c_api_experimental.h"
#include "tensorflow/c/tf_datatype.h"
#include "tensorflow/core/platform/logging.h"
#include "tensorflow/core/platform/tstring.h"

==============================================================================*/

#include "tensorflow/c/eager/c_api_experimental.h"

#include <cstdint>
#include <memory>
#include <utility>
#include <vector>

#include "absl/container/flat_hash_map.h"
#include "absl/strings/match.h"
#include "absl/time/time.h"
#include "tensorflow/c/c_api.h"
#include "tensorflow/c/eager/c_api_internal.h"

    EXPECT_TRUE(found);
    TF_DeleteBuffer(op_list_buffer);
  }
}

void TestEncodeDecode(int line, const std::vector<string>& data) {
  const int64_t n = data.size();
  Status status;
  for (const std::vector<int64_t>& dims :
       std::vector<std::vector<int64_t>>{{n}, {1, n}, {n, 1}, {n / 2, 2}}) {
    // Create C++ Tensor
    Tensor src(tensorflow::DT_STRING, TensorShape(dims));

limitations under the License.
==============================================================================*/

#include "tensorflow/c/eager/c_api_unified_experimental.h"

#include <vector>

#include "absl/container/flat_hash_map.h"
#include "absl/strings/str_cat.h"
#include "tensorflow/c/eager/c_api_unified_experimental_internal.h"
#include "tensorflow/c/tf_datatype.h"
#include "tensorflow/c/tf_status.h"

// dtype of `inputs`.
Status CreateParamsForInputs(AbstractContext* ctx,
                             absl::Span<AbstractTensorHandle* const> inputs,
                             std::vector<AbstractTensorHandle*>* params);

// A callable that takes tensor inputs and returns zero or more tensor outputs.
using Model = std::function<Status(AbstractContext*,