// CHECK-DAG:       [[VAL_33:%.*]] = arith.constant dense<[-1, 0, 0]> : tensor<3xi32>...

  // CHECK: "tf.Conv3DBackpropInputV2"
}

func.func @mul_i64(%arg0: tensor<14xi64>, %arg1: tensor<14xi64>) -> tensor<14xi64> {
  %0 = "tf.Mul"(%arg0, %arg1) : (tensor<14xi64>, tensor<14xi64>) -> tensor<14xi64>
  func.return %0: tensor<14xi64>

// CHECK-LABEL: mul_i64
// CHECK:  tfl.mul %arg0, %arg1 {fused_activation_function = "NONE"} : tensor<14xi64>
// CHECK:  return
}

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-LABEL: testMultinomial
func.func @testMultinomial(%arg0: tensor<2xf32>, %arg1: tensor<1xi32>) -> tensor<10xi64> {
  // CHECK: "tfl.multinomial"(%arg0, %arg1)
  %0 = "tfl.multinomial"(%arg0, %arg1) {seed = 0 : i64, seed2 = 0: i64} : (tensor<2xf32>, tensor<1xi32>) -> tensor<10xi64>
  func.return %0 :  tensor<10xi64>
}

// -----

// CHECK-LABEL: testMultinomialInt32

  // 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 =