func.func @replicate_arg_shape(%arg0: tensor<*xf32>, %arg1: tensor<*xf32>) {
  %0:4 = tf_device.replicate([%arg0, %arg1] as %ri: tensor<*xf32>) {n = 2: i32} {
    %1 = "tf.Shape"(%ri) {device = "", T = "tfdtype$DT_FLOAT", out_type = "tfdtype$DT_INT32"} : (tensor<*xf32>) -> tensor<?xi32>
    %2 = "tf.opA"(%1, %ri) : (tensor<?xi32>, tensor<*xf32>) -> tensor<*xi32>
    tf_device.return %1, %2 : tensor<?xi32>, tensor<*xi32>
  }
  func.return
}

  // ALWAYS-DECOMPOSE-NOT:  "tf.AssignAddVariableOp"
  "tf.AssignAddVariableOp"(%0, %1) {dtype = "tfdtype$DT_INT32"} : (tensor<*x!tf_type.resource<tensor<2x8xi32>>>, tensor<i32>) -> ()
  // CHECK: tf_device.cluster
  "tf_device.cluster"() ({
    // CHECK-NOT: "tf.AssignAddVariableOp"
    "tf.AssignAddVariableOp"(%0, %1) {dtype = "tfdtype$DT_INT32"} : (tensor<*x!tf_type.resource<tensor<2x8xi32>>>, tensor<i32>) -> ()
    tf_device.return
  }) : () -> ()

  %0 = "tf.Shape"(%arg0) {T = "tfdtype$DT_FLOAT", output = "tfdtype$DT_INT32"} : (tensor<f32>) -> tensor<0xi32>

  // Result shape need not be static. Folding harness uses TensorFlow constant
  // in that case.
  // CHECK-DAG: "tf.Const"() <{value = dense<[1, 32, 32, 16]> : tensor<4xi32>}> : () -> tensor<?xi32>
  %1 = "tf.Shape"(%arg1) {T = "tfdtype$DT_FLOAT", output = "tfdtype$DT_INT32"} : (tensor<1x32x32x16xf32>) -> tensor<?xi32>

^bb0(%arg0: tensor<1x32x32x16xf32>, %arg1: tensor<*xf32>):
  %0 = "tf.Shape"(%arg0) {T = "tfdtype$DT_FLOAT", output = "tfdtype$DT_INT32"} : (tensor<1x32x32x16xf32>) -> tensor<4xi32>
  %1 = "tf.Shape"(%arg1) {T = "tfdtype$DT_FLOAT", output = "tfdtype$DT_INT32"} : (tensor<*xf32>) -> tensor<?xi32>
  func.return %0, %1 : tensor<4xi32>, tensor<?xi32>
}

// -----

  %cst_1 = arith.constant dense<-1> : tensor<i32>
  %0 = "tf.TensorListFromTensor"(%arg0, %cst) : (tensor<2x3xf32>, tensor<1xi32>) -> tensor<!tf_type.variant<tensor<3xf32>>>
  %1:2 = "tf.While"(%cst_0, %0) {T = ["tfdtype$DT_INT32", "tfdtype$DT_VARIANT"], body = @tensorlistWhileBody, cond = @tensorlistWhileCond, is_stateless = false} : (tensor<i32>, tensor<!tf_type.variant<tensor<3xf32>>>) -> (tensor<i32>, tensor<!tf_type.variant<tensor<*xf32>>>)

}

func.func @const() -> tensor<2xi32> {

// -----

// CHECK-LABEL: @const
func.func @const() -> tensor<2xi32> {
  // CHECK: mhlo.constant dense<0> : tensor<2xi32>
  %0 = "tf.Const"() {device = "", name = "", dtype = "tfdtype$DT_INT32", value = dense<0> : tensor<2xi32>} : () -> (tensor<2xi32>)
  func.return %0: tensor<2xi32>
}

// -----

// CHECK-LABEL: @const_dynamic_output
func.func @const_dynamic_output() -> tensor<*xi32> {

    // CHECK: tf.BroadcastGradientArgs
    // CHECK-SAME: (tensor<*xi32>, tensor<0xi32>) -> (tensor<?xi32>, tensor<?xi32>)
    %2:2 = "tf.BroadcastGradientArgs"(%arg0, %arg1) {T = "tfdtype$DT_INT32", name = "BroadcastGradientArgs"} : (tensor<*xi32>, tensor<0xi32>) -> (tensor<?xi32>, tensor<?xi32>)
    func.return %2#0 : tensor<?xi32>
  }

  // CHECK-LABEL: func @shape_from_const_input