tensorflow::JobDef* job_def = server_def->mutable_cluster()->mutable_job(0);
  job_def->mutable_tasks()->at(task_index) = absl::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);

     *
     * @param context the query context containing search configuration
     * @param query the Lucene query to convert (must be a FuzzyQuery)
     * @param boost the boost factor to apply to the query
     * @return OpenSearch QueryBuilder for fuzzy matching
     * @throws InvalidQueryException if the query is not a FuzzyQuery
     */
    @Override

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

#ifndef TENSORFLOW_C_EAGER_C_API_UNIFIED_EXPERIMENTAL_INTERNAL_H_
#define TENSORFLOW_C_EAGER_C_API_UNIFIED_EXPERIMENTAL_INTERNAL_H_

#include <vector>

#include "tensorflow/c/c_api.h"
#include "tensorflow/c/conversion_macros.h"
#include "tensorflow/c/eager/abstract_context.h"
#include "tensorflow/c/eager/abstract_operation.h"

    } else {
      TFE_OpAddInput(op, input, s);
    }
    if (TF_GetCode(s) != TF_OK) return;
  }
  std::vector<TFE_TensorHandle*> op_outputs(*num_outputs);
  TFE_Execute(op, op_outputs.data(), num_outputs, s);
  TFE_DeleteOp(op);
  if (TF_GetCode(s) != TF_OK) return;
  std::vector<TFE_TensorHandle*> unwrapped_outputs;
  unwrapped_outputs.reserve(op_outputs.size());
  for (auto* handle : op_outputs) {

	MOVL (AX)(X0*1), AX             // ERROR "invalid instruction"
	MOVL (AX)(Y0*1), AX             // ERROR "invalid instruction"
	// VSIB/VM is invalid without vector index.
	// TODO(quasilyte): improve error message (#21860).
	// "invalid VSIB address (missing vector index)"
	VPGATHERQQ Y2, (BP), Y1         // ERROR "invalid instruction"
	// AVX2GATHER mask/index/dest #UD cases.

        assertTrue(result.contains("sectPerAlloc=" + sectPerAlloc));
        assertTrue(result.contains("bytesPerSect=" + bytesPerSect));
    }

    @Test
    @DisplayName("Test decode with single sector per allocation")
    void testDecodeWithSingleSectorPerAllocation() throws SMBProtocolDecodingException {
        // Prepare test data with sectPerAlloc = 1
        byte[] buffer = new byte[22];

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

void TestEncodeDecode(int line, const std::vector<std::string>& data) {
  const int64_t n = data.size();
  absl::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));

          command: [
            "/bin/sh",
            "-ce",

            buffer.putLong(524288L); // Free allocation units (512K)
            buffer.putInt(8); // Sectors per allocation unit
            buffer.putInt(512); // Bytes per sector
            byte[] bufferArray = buffer.array();

            // When
            int bytesConsumed = fileFsSizeInfo.decode(bufferArray, 0, bufferArray.length);

            // Then

      - name: Setup tmate session
        uses: mxschmitt/action-tmate@v3
        if: ${{ github.event_name == 'workflow_dispatch' && github.event.inputs.debug_enabled == 'true' }}
        with:
          limit-access-to-actor: true
        env:
          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
      - name: Notify Translations
        run: uv run ./scripts/notify_translations.py
        env:
          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}