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

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>)

  attributes {tf_saved_model.exported_names = ["test_hoist_varhandleop"]} {
  // CHECK-NOT: tf.VarHandleOp
  // CHECK-NOT: tf.ReadVariableOp
  // CHECK: [[v:%.*]] = "tf._TfrtGetResource"() <{container = [""], indices = [0], shared_name = [""]}> {device = "/CPU:0"} : () -> tensor<i32>
  // CHECK: [[r:%.*]] = "tf.AddV2"({{.*}}, [[v]]) {device = "/CPU:0"} : (tensor<i32>, tensor<i32>) -> tensor<i32>
  // CHECK: return [[r]]

  let summary = "get a tensor in resource array";

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

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

  let arguments = (ins
    I64ArrayAttr:$indices
  );

  let results = (outs
    Variadic<TFTensorType>:$results
  );

				// best position for error reporting
				pos := ix.Pos()
				if i < len(ix.Indices) {
					pos = ix.Indices[i].Pos()
				}
				check.softErrorf(atPos(pos), InvalidTypeArg, "%v", err)
			} else {
				check.mono.recordInstance(check.pkg, ix.Pos(), inst.TypeParams().list(), inst.TypeArgs().list(), ix.Indices)
			}
		}

		// TODO(rfindley): remove this call: we don't need to call validType here,

            List<Integer> indices = new ArrayList<>();
            List<String> tgt = target.getModules();
            Set<String> excludes = new LinkedHashSet<>(tgt);
            List<String> merged = new ArrayList<>(tgt.size() + src.size());
            merged.addAll(tgt);
            for (int i = 0, n = tgt.size(); i < n; i++) {
                indices.add(i);
            }