// CHECK-DAG:       [[VAL_21:%.*]] = arith.constant dense<[-1,...

}

func.func @sigmoid(%arg0: tensor<?x88xf32>) -> tensor<?x88xf32> {
  %0 = "tf.Sigmoid"(%arg0) : (tensor<?x88xf32>) -> tensor<?x88xf32>
  func.return %0 : tensor<?x88xf32>
// CHECK-LABEL: sigmoid
// CHECK:  "tfl.logistic"(%arg0) : (tensor<?x88xf32>) -> tensor<?x88xf32>
}

func.func @sqrt(%arg0: tensor<8x16xf32>) -> tensor<8x16xf32> {

// CHECK-LABEL: testBasicLstm
func.func @testBasicLstm(%arg0: tensor<1x384xf32>, %arg1: tensor<1x96xf32>, %arg2: tensor<384x480xf32>, %arg3: tensor<384xf32>, %arg4: tensor<1x96xf32>) -> (tensor<1x96xf32>, tensor<1x96xf32>) {

// CHECK-LABEL: testFoldEnsureShapeOp
func.func @testFoldEnsureShapeOp(%arg0: tensor<10x20xf32>) -> (tensor<10x20xf32>, tensor<10x20xf32>, tensor<20x10xf32>) {
  %0 = "tf.EnsureShape"(%arg0) {shape = #tf_type.shape<10x20>} : (tensor<10x20xf32>) -> tensor<10x20xf32>
  %1 = "tf.EnsureShape"(%arg0) {shape = #tf_type.shape<?x20>} : (tensor<10x20xf32>) -> tensor<10x20xf32>
  // Failing case which should not be folded.

    } else {
      // Recurse on the subtypes in the variant/resource. Basically if the input
      // were:
      //   tensor<!tf_type.variant<tensor<?x8xf32>>>
      // and:
      //   tensor<!tf_type.variant<tensor<10x8xf32>>>
      // we'll try here to refine tensor<?x8xf32> with tensor<10x8xf32>.
      auto refined_subtype = mlir::cast<TensorType>(
          TypeMeet(lhs_element_type_with_subtype.GetSubtypes().front(),

    ```mlir
      %0 = "tf.Const"() {value = dense<[[42.0]]> : tensor<1x1xf32>} : () -> tensor<1x1xf32>
      %1 = "tf.Const"() {device = "", value = dense<[[42.0]]> : tensor<1x1xf32>} : () -> tensor<1x1xf32>
      %2 = "tf.Const"() {device = "baz", value = dense<[[42.0]]> : tensor<1x1xf32>} : () -> tensor<1x1xf32>
    ```

    then running this pass with 'default-device=foobar', we get:

    ```mlir