// CHECK-DAG-SAME: strides = dense<1> : tensor<1xi64>} : (tensor<2xi64>) -> tensor<1xi64>
  // CHECK: %[[RESHAPED_START2:.*]] = mhlo.reshape %[[SLICED_START2]] : (tensor<1xi64>) -> tensor<i64>
  // CHECK: %[[RESULT:.*]] = "mhlo.dynamic_slice"(%arg0, %[[RESHAPED_START1]], %[[RESHAPED_START2]]) <{slice_sizes = dense<[1, 4]> : tensor<2xi64>}> : (tensor<3x4xi32>, tensor<i64>, tensor<i64>) -> tensor<1x4xi32>
  // CHECK: return %[[RESULT]] : tensor<1x4xi32>

      (field_name == "broadcast_dimensions" ||
       field_name == "known_expanding_dimensions" ||
       field_name == "known_nonexpanding_dimensions"))
    return true;
  if ((op_name == "stablehlo.dynamic_slice" || op_name == "stablehlo.gather") &&
      field_name == "slice_sizes")
    return true;
  if (op_name == "stablehlo.fft" && field_name == "fft_length") return true;

  %handle_0, %flow_0 = "tf.TensorArrayV3"(%size) {clear_after_read = true, device = "/job:localhost/replica:0/task:0/device:CPU:0", dtype = f32, dynamic_size = false, element_shape = #tf_type.shape<?x100>, identical_element_shapes = true, tensor_array_name = "processed_embeddings/bidirectional_rnn/bw/bw/dynamic_rnn/input_0"} : (tensor<i32>) -> (tensor<2x!tf_type.resource<tensor<?x100xf32>>>, tensor<f32>)...

                          Const(zero_32))));

  std::vector<string> compile_time_constant_inputs;
  compile_time_constant_inputs.push_back("size");
  auto m_dynamic_slice = NodeWith(
      Op("Slice"), AssignedDevice(kDeviceName),
      Attr(kXlaCompileTimeConstantInputsAttr, compile_time_constant_inputs),
      Inputs(m_input, m_begin_s64, m_dynamic_slice_size));

//CHECK-NEXT:}

func.func @dyanmic_slice(%arg0: tensor<3x3xi64>, %arg1: tensor<i64>, %arg2: tensor<i64>) -> tensor<3x3xi64> {
  %0 = "vhlo.dynamic_slice_v1"(%arg0, %arg1, %arg2) <{
    slice_sizes = #vhlo.tensor_v1<dense<[3, 3]> : tensor<2xi64>>
  }> : (tensor<3x3xi64>, tensor<i64>, tensor<i64>) -> tensor<3x3xi64>
  return %0 : tensor<3x3xi64>
}