SmallVector<int64_t> indices(index_map_size);
  for (int64_t i = 0; i < index_map_size; i++) {
    indices[i] = dimension_numbers.start_index_map()[i];
  }

  // Fill elements from start_indices with start_index_map
  Value scattered_start_indices = builder.create<TF::TensorScatterUpdateOp>(
      loc, empty_start_indices,
      /*indices=*/
      builder.create<TF::ReshapeOp>(

    // CHECK: "tf.AssignVariableOp"([[VAR]], [[TENSOR]])
    "tf.ResourceScatterAdd"(%resource, %indices, %updates) : (tensor<*x!tf_type.resource<tensor<*xi32>>>, tensor<2x?xi32>, tensor<?x?x?xi32>) -> ()

    tf_device.return
  }) : () -> ()
  func.return
}

// Tests that composite tf.ResourceScatterAdd with 1d Indices operation is decomposed.

// CHECK-LABEL: @decompose_resource_scatter_add_op_1d_indices

  SmallVector<TransposeOp, 2> transpose_ops;

  // Constant operation that defines permutation indices for result transposes.
  ConstOp permutation_op;

  // All operation results must be used by transpose operations with the same
  // permutation indices.
  for (OpResult result : op->getResults()) {
    for (Operation* user : result.getUsers()) {

  let description = [{
    The op compiles and executes a GPU cluster function.

    func_name is the name of the function to be executed on GPU.
    resource_indices are the indices of inputs that are resources.
    used_output_indices are the indices of outputs that have users.

    Example:
      %results = gpurt.compile_and_execute {func_name = "xla_func_0", resource_indices = [1] ...}
  }];

  let arguments = (ins

  // CHECK: [[ch3:%.*]], [[results:%.*]]:2 = tfrt_fallback_async.get_resource [[ready_ch]] {device = "/device:CPU:0", indices = [0, 1]}
  // CHECK: tfrt_fallback_async.executeop key({{.*}}) cost({{.*}}) device("/device:CPU:0") "tf.MatMul"([[results]]#0, [[results]]#1)
  %a, %b = "tf._TfrtGetResource"() {device = "/device:CPU:0", indices = [0, 1], shared_name = ["", ""], container = ["", ""]} : () -> (tensor<3x1xf32>, tensor<1x3xf32>)

//
// values, indices = tfl.topkv2(%inputs, K)
// %1 = tfl.slice(values, 0, k)
// %2 = tfl.slice(indices,0, k)
// .... (values and indices only used for %1 and %2)
//
// %1 or %2 can be absent. If values and indices are only used here,
// this pattern can be replaced with (conceptually)
//
// %values, %indices = tfl.topkv2(%inputs, k)
// replace all use of %1 with values

      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();
}

    Stateful operands correspond to TF's variables semantics. An op that has 1
    or more stateful operands is a stateful op.
  }];

  let methods = [
    InterfaceMethod<
      [{Returns the indices of stateful operands.}],
      "std::vector<int>", "GetStatefulOperands", (ins)
    >,
  ];
}

//===----------------------------------------------------------------------===//
// TFL op interface for sparse operands.

	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.