func.return %1 : tensor<10x3x2xf32>

// CHECK-LABEL:scatterNdVectorIndices
// CHECK: %[[CST:.*]] = arith.constant dense<[10, 3, 2]> : tensor<3xi32>
// CHECK: %[[RES:.*]] = "tfl.scatter_nd"(%arg0, %arg1, %[[CST]]) : (tensor<5x1xi32>, tensor<5x3x2xf32>, tensor<3xi32>) -> tensor<10x3x2xf32>
// CHECK: return %[[RES]]
}

      indices_type.getDimSize(1) != 1)
    return false;

  // Checks the `params_type` is equal to `output_type`. If not equal, we
  // cannot replace the scatter_nd/gather_nd op with `params`.
  if (params_type != output_type) return false;

  // Checks the value in `indices` is from 0 to n-1.
  int cur_value = 0;
  for (const auto &v : indices.getValues<APInt>()) {

  );
}

def TFL_ScatterNdOp : TFL_Op<"scatter_nd", [
    Pure,
    QuantizableResult,
    SameOperandsAndResultsScale,
    TFL_OperandHasAtleastRank<0, 1>,
    TFL_OperandHasAtleastRank<1, 1>,
    PredOpTrait<"updates and output must have same element type",
      TFL_TCresVTEtIsSameAsOp<0, 1>>
  ]> {
  let summary = "Scatter_nd operator";

  let description = [{

}

// -----

// CHECK-LABEL: scatter_nd_i1
func.func @scatter_nd_i1(%arg0: tensor<?xi32>, %arg1: tensor<?xi1>, %arg2: tensor<?xi32>) -> tensor<?xi1> {
  // CHECK: "tfl.scatter_nd"(%arg0, %arg1, %arg2)
  %0 = "tfl.scatter_nd"(%arg0, %arg1, %arg2): (tensor<?xi32>, tensor<?xi1>, tensor<?xi32>) -> tensor<?xi1>
  func.return %0 : tensor<?xi1>
}

// -----

// CHECK-LABEL: testUnsortedSegmentSum