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

// 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> {
^bb0(%arg0: tensor<1xf32>, %arg1: tensor<1xf32>):
  %0 = "tf.SquaredDifference"(%arg0, %arg1) : (tensor<1xf32>, tensor<1xf32>) -> tensor<1xf32>
  %1 = "tf.Relu6"(%0) : (tensor<1xf32>) -> tensor<1xf32>

// 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 @testLeakyWrongAlphaType(tensor<16xf32>) -> tensor<16xf32> {
^bb0(%arg0: tensor<16xf32>):

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:           }) : (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>

    // 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>

          [%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}}

DATA ·IEEEConst+1544(SB)/8,$0x0000000090db8c44

	/* x^161856 mod p(x), x^161792 mod p(x) */
DATA ·IEEEConst+1552(SB)/8,$0x0000000067a2c786
DATA ·IEEEConst+1560(SB)/8,$0x000000010010a4ce

	/* x^160832 mod p(x), x^160768 mod p(x) */
DATA ·IEEEConst+1568(SB)/8,$0x0000000048b9496c
DATA ·IEEEConst+1576(SB)/8,$0x00000001c8f4c72c

	/* x^159808 mod p(x), x^159744 mod p(x) */
DATA ·IEEEConst+1584(SB)/8,$0x000000015a422de6

  // Look for resource or variant element type and ensure we refine the subtype.
  // We only support a single subtype at the moment, we won't handle something
  // like:
  //   tensor<!tf_type.variant<tensor<10xf32>, tensor<8xf32>>
  if (rhs_element_type_with_subtype &&
      rhs_element_type_with_subtype.GetSubtypes().size() == 1) {
    auto lhs_element_type_with_subtype =