"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": {

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

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"

    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*,

  job_def->mutable_tasks()->at(task_index) =
      tensorflow::strings::StrCat(host, ":", port);
}

std::vector<std::string> ListDeviceNames(TFE_Context* ctx) {
  TF_Status* status = TF_NewStatus();
  std::vector<std::string> device_names;
  TF_DeviceList* devices = TFE_ContextListDevices(ctx, status);
  EXPECT_EQ(TF_GetCode(status), TF_OK) << TF_Message(status);

	// difference that for some j, a[j]>b[j] and a[j+1]<b[j+1]. If the implementation
	// compares large chunks with wrong endianness, it gets wrong result.
	// no vector register is larger than 512 bytes for now
	const maxLength = 512
	a := make([]byte, maxLength)
	b := make([]byte, maxLength)
	// randomish but deterministic data. No 0 or 255.
	for i := 0; i < maxLength; i++ {