%multiples = "tf.Const"() { device = "/device:CPU:0", value = dense<[7,3]> : tensor<2xi32> } : () -> tensor<2xi32>
  // CHECK: T = !corert.string
  %output = "tf.Tile"(%arg, %multiples) { device = "/device:CPU:0" } : (tensor<1x2x!tf_type.string>, tensor<2xi32>) -> tensor<?x6x!tf_type.string>
  func.return %output : tensor<?x6x!tf_type.string>
}

// CHECK-LABEL: func @shape
func.func @shape() {

# CHECK-DAG:       %[[VAL_2:.*]] = arith.constant dense<[1, 0]> : tensor<2xi32>
# CHECK-DAG:       %[[VAL_3:.*]] = "tfl.no_value"() <{value}> : () -> none
# CHECK-DAG:       %[[VAL_6:.*]] = arith.constant dense<0> : tensor<i32>

  OwningOpRef<ModuleOp> module_op = ParseModuleOpString(R"mlir(
    module {
      "tf_saved_model.global_tensor"() {type = tensor<2xi32>, value = dense<2> : tensor<2xi32>, sym_name = "__tf_saved_model_x"} : () -> ()
      func.func @main(%arg_0: tensor<!tf_type.resource<tensor<2xi32>>> {tf_saved_model.bound_input = @__tf_saved_model_x}) -> () attributes {tf.entry_function = {inputs = "arg_0:0", outputs = ""}} {
        return
      }

    %cst_0 = "tf.Const"() {value = dense<[-1, 10]> : tensor<2xi32>} : () -> tensor<2xi32>
    %1 = "tf.MatMul"(%arg0, %arg1) {
      transpose_a = false, transpose_b = false
    } : (tensor<1x10xf32>, tensor<10x10xf32>) -> tensor<1x10xf32>  loc(fused["MatMul:", "test_opt_out"])
    %2 = "tf.Reshape"(%1, %cst_0) : (tensor<1x10xf32>, tensor<2xi32>) -> tensor<?x10xf32> loc(fused["Reshape:", "model/reshape"])

# CHECK-SAME: tf_saved_model.initializer_type = "init_op"
# CHECK-SAME: }
# CHECK-DAG: %[[CST_0:.*]] = "tf.Const"() <{{{.*dense<.*> : tensor<2xi32>.*}}}> {{{.*}}} : () -> tensor<2xi32>
# CHECK: %[[RAND_STD_NORMAL:.*]] = "tf.RandomStandardNormal"(%[[CST_0]])
# CHECK: "tf.AssignVariableOp"(%[[ARG_0]], %[[RAND_STD_NORMAL]]){{.*}}: (tensor<!tf_type.resource<tensor<1x3xf32>>>, tensor<1x3xf32>) -> ()

      %outputs_6, %control_7 = tf_executor.island wraps "tf.Const"() {device = "", value = dense<[-1, 16384]> : tensor<2xi32>} : () -> tensor<2xi32>

}

func.func @while_body_11_frozen0(%arg0: tensor<*xi32>, %arg1: tensor<*xi32>, %arg2: tensor<*xi32>, %arg3: tensor<*xf32>, %arg4: tensor<*xf32>) -> (tensor<*xi32>, tensor<*xi32>, tensor<*xi32>, tensor<*xf32>, tensor<*xf32>) {
  %cst = arith.constant dense<[1, 0]> : tensor<2xi32>
  %cst_0 = arith.constant dense<0> : tensor<i32>
  %cst_1 = arith.constant dense<-1> : tensor<i32>

// CHECK:   %{{.*}} = "tfl.dequantize"(%{{.*}}) : (tensor<1x401408x!quant.uniform<u8:f32, 3.906250e-03>>) -> tensor<1x401408xf32>

  %cst = arith.constant dense<[1, 401408]> : tensor<2xi32>
  %0 = "tfl.quantize"(%arg0) {qtype = tensor<1x224x224x3x!quant.uniform<u8:f32, 7.812500e-03:128>>} : (tensor<1x224x224x3xf32>) -> tensor<1x224x224x3x!quant.uniform<u8:f32, 7.812500e-03:128>>

class TestModule(tf.Module):

  def __init__(self):
    super(TestModule, self).__init__()
    # CHECK: "tf_saved_model.global_tensor"() <{is_mutable, {{.*}} type = tensor<*xf32>, value = dense<0.000000e+00> : tensor<1xf32>}> {tf_saved_model.exported_names = ["v0"]} : () -> ()

  } : (tensor<i32>) -> (tensor<2xi32>, tensor<*xf32>)

// CHECK: "tf.TensorArrayV3"
// CHECK-SAME: : (tensor<i32>) -> (tensor<2x!tf_type.resource>, tensor<f32>)

  %1 = "tfl.cast"(%0#1) : (tensor<*xf32>) -> tensor<f32>
  func.return %1 : tensor<f32>
}