input_nodes->insert({node, {idx}});
    } else {
      auto& indices = iter->second;
      if (std::find(indices.begin(), indices.end(), idx) != indices.end()) {
        return InvalidArgument("TF_Output ", node->name(), ":", idx,
                               " appears more than once in the input list");
      }
      indices.push_back(idx);
    }
  }
  return absl::OkStatus();
}

			dur := Since(now)
			if dur > 750*Millisecond {
				t.Errorf("timer %d took %v to complete", i, dur)
			}

		// Timer is done. Swap with tail and remove.
		case 10:
			indices[ii] = indices[len(indices)-1]
			indices = indices[:len(indices)-1]
		}
	}
}

// Benchmark timer latency when the thread that creates the timer is busy with
// other work and the timers must be serviced by other threads.

import org.opensearch.action.ActionRequest;
import org.opensearch.action.ActionType;
import org.opensearch.action.admin.cluster.health.ClusterHealthResponse;
import org.opensearch.action.admin.indices.segments.IndicesSegmentResponse;
import org.opensearch.action.admin.indices.segments.PitSegmentsRequest;
import org.opensearch.action.bulk.BulkRequest;
import org.opensearch.action.bulk.BulkRequestBuilder;
import org.opensearch.action.bulk.BulkResponse;

	res, err := c.Indices.ResolveIndex([]string{args.Index})
	if err != nil {
		return err
	}
	defer res.Body.Close()

	var v map[string]interface{}
	found := false
	if err := json.NewDecoder(res.Body).Decode(&v); err != nil {
		return fmt.Errorf("Error parsing response body: %v", err)
	}

	indices, ok := v["indices"].([]interface{})
	if ok {

//===----------------------------------------------------------------------===//

def LowerTensorScatterAdd: Pat<
  (TF_TensorScatterAddOp $input, $indices, $updates),
  (TF_TensorScatterUpdateOp $input, $indices,
     (TF_AddOp $updates, (CreateGatherNdOp $updates, $input, $indices)))>;

//===----------------------------------------------------------------------===//
// AddV2 op patterns.

      op->setAttr(attr.getName(),
                  FlatSymbolRefAttr::get(op->getContext(), cloned.getName()));
      break;
    }
  }

  // Traverse results backward so that indices to be deleted stay unchanged.
  for (OpResult result : llvm::reverse(op->getResults())) {
    if (!result.use_empty()) continue;
    int result_idx = result.getResultNumber();
    for (func::FuncOp func : cloned_branches)

    // final_op = "Div"
    llvm::SmallVector<int64_t> indices(group_size.getInt());
    std::iota(indices.begin(), indices.end(), 0);

    auto replica_groups = mlir::DenseIntElementsAttr::get(
        mlir::RankedTensorType::get({1, group_size.getInt()},
                                    rewriter.getI64Type()),
        indices);

    {

            doIndexCreate = true;
            doCreate = true;
        }

        if (doCreate) {
            if (doIndexCreate) {
                try {
                    client.admin().indices().prepareCreate(settingsIndexName).setSettings(loadIndexSettings(), XContentType.JSON).execute()
                            .actionGet(getIndicesTimeout());
                } catch (final IOException e) {

  let description = [{
    Get a tensor in resource array.

    indices: the indices in the resource array.
    results: the tensor values for the corresponding indices.

    in_ch and out_ch are for control dependencies.
  }];

  let arguments = (ins
    TFRT_ChainType:$in_ch,
    StrAttr:$device,
    I64ArrayAttr:$indices
  );

  let results = (outs
    TFRT_ChainType:$out_ch,

}

// CHECK-LABEL: func @tensor_array_while_test
// CHECK-SAME: ([[in_chain:%.*]]: !tfrt.chain
func.func @tensor_array_while_test(%indices: tensor<?xi32>, %input_0: tensor<?x?x?xf32>, %input_1: tensor<?x?x?xf32>) -> (tensor<?x?x512xf32>, tensor<?x?x512xf32>) {
  %index = "tf.Const"() {device = "/device:CPU:0", value = dense<0> : tensor<i32>} : () -> (tensor<i32>)