// CHECK:           }) : (tensor<1x?xf32>, tensor<1x0xf32>, tensor<1x0xf32>, tensor<1x0xf32>, tensor<1x0xf32>, tensor<1x3xf32>, tensor<1x3xf32>, tensor<1x3xf32>, tensor<1x3xf32>, none, none, none, tensor<1xf32>, tensor<1xf32>, tensor<1xf32>, tensor<1xf32>, tensor<3x1xf32>, tensor<3xf32>, tensor<1x3xf32>, tensor<1x1xf32>, tensor<1xf32>, tensor<1xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x3xf32>

func.func @testCompatibleCastType(%arg0: tensor<?xf32>) -> (tensor<10xf32>, tensor<10xf32>) {
  %0 = "tf.Cast"(%arg0) {Truncate = false} : (tensor<?xf32>) -> tensor<10xf32>
  %1 = "tf.Cast"(%arg0) {Truncate = true} : (tensor<?xf32>) -> tensor<10xf32>
  func.return %0, %1: tensor<10xf32>, tensor<10xf32>

  // CHECK: %0 = "tf.Cast"(%arg0) <{Truncate = false}> : (tensor<?xf32>) -> tensor<10xf32>

    // CHECK: %[[MUL:.*]] = "tf.Mul"{{.*}} (tensor<1xf32>, tensor<1xf32>) -> tensor<1xf32>
    // CHECK: %[[UNKNOWN:.*]] = "tf.Unknown"(%[[MUL]], %[[MUL]]) : (tensor<1xf32>, tensor<1xf32>) -> tensor<*xf32>
    // CHECK: return %[[UNKNOWN]] : tensor<*xf32>
    %0 = "tf.Mul"(%arg0, %arg0) : (tensor<1xf32>, tensor<1xf32>) -> tensor<*xf32>
    %1 = "tf.Unknown"(%0, %0) : (tensor<*xf32>, tensor<*xf32>) -> tensor<*xf32>

  %0 = "tfl.unidirectional_sequence_lstm"(%arg0,...

  func.return %0 : tensor<16x16x16x16xf32>
}

// -----

// Test valid tf.LeakyRelu
// CHECK-LABEL: func @testLeakyRelu(%arg0: tensor<16xf32>)
func.func @testLeakyRelu(tensor<16xf32>) -> tensor<16xf32> {
^bb0(%arg0: tensor<16xf32>):
  %0 = "tf.LeakyRelu"(%arg0) {alpha = 0.2 : f32} : (tensor<16xf32>) -> tensor<16xf32>
  func.return %0 : tensor<16xf32>
}

}

func.func @div(%arg0: tensor<1xf32>, %arg1: tensor<1xf32>) -> tensor<1xf32> {
  %0 = "tf.Div"(%arg0, %arg1) : (tensor<1xf32>, tensor<1xf32>) -> tensor<1xf32>
  func.return %0: tensor<1xf32>

// CHECK-LABEL: div
// CHECK:  tfl.div %arg0, %arg1 {fused_activation_function = "NONE"} : tensor<1xf32>
// CHECK:  return
}

func.func @squaredDifferenceRelu(tensor<1xf32>, tensor<1xf32>) -> tensor<1xf32> {

    } 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(),

          [%u0#0, %u0#1] as %u : tensor<32xf32>)
          {n = 2 : i32, devices = {CORE_0 = ["/CPU:0", "/GPU:1"]}} {
        %read0 = "tf.ReadVariableOp"(%r0) : (tensor<*x!tf_type.resource<tensor<32xf32>>>) -> tensor<32xf32>
        // expected-remark@above {{ID: 1}}
        "tf.AssignVariableOp"(%r1, %u) : (tensor<*x!tf_type.resource<tensor<32xf32>>>, tensor<32xf32>) -> ()
        // expected-remark@above {{ID: 2}}

	0xe9b: 0x007a, 0xe9c: 0x006a,
	// Block 0x3b, offset 0xec0
	0xed2: 0x000c, 0xed3: 0x000c, 0xed4: 0x000c,
	0xef2: 0x000c, 0xef3: 0x000c, 0xef4: 0x000c,
	// Block 0x3c, offset 0xf00
	0xf12: 0x000c, 0xf13: 0x000c,
	0xf32: 0x000c, 0xf33: 0x000c,
	// Block 0x3d, offset 0xf40
	0xf74: 0x000c, 0xf75: 0x000c,
	0xf77: 0x000c, 0xf78: 0x000c, 0xf79: 0x000c, 0xf7a: 0x000c, 0xf7b: 0x000c,
	0xf7c: 0x000c, 0xf7d: 0x000c,

	0xe80: 0x0009,
	0xe9b: 0x007a, 0xe9c: 0x006a,
	// Block 0x3b, offset 0xec0
	0xed2: 0x000c, 0xed3: 0x000c, 0xed4: 0x000c,
	0xef2: 0x000c, 0xef3: 0x000c,
	// Block 0x3c, offset 0xf00
	0xf12: 0x000c, 0xf13: 0x000c,
	0xf32: 0x000c, 0xf33: 0x000c,
	// Block 0x3d, offset 0xf40
	0xf74: 0x000c, 0xf75: 0x000c,
	0xf77: 0x000c, 0xf78: 0x000c, 0xf79: 0x000c, 0xf7a: 0x000c, 0xf7b: 0x000c,
	0xf7c: 0x000c, 0xf7d: 0x000c,