func.func @simple_chain_with_broadcast(%arg0: tensor<1xf32>, %arg1: tensor<10xf32>) -> tensor<?xf32> {
    // CHECK: %[[MUL:.*]] = "tf.Mul"{{.*}} (tensor<1xf32>, tensor<10xf32>) -> tensor<10xf32>
    // CHECK: %[[ADD:.*]] = "tf.Add"(%[[MUL]], %[[MUL]]) : (tensor<10xf32>, tensor<10xf32>) -> tensor<10xf32>
    // CHECK: %[[CAST:.*]] = "tf.Cast"(%[[ADD]]) {{.*}} : (tensor<10xf32>) -> tensor<*xf32>

// 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>

	0x350: 0xe7, 0x351: 0xe8, 0x352: 0xe9, 0x353: 0xea, 0x356: 0xeb, 0x357: 0xec,
	0x358: 0xed, 0x359: 0xee, 0x35a: 0xef, 0x35b: 0xf0, 0x35c: 0xf1,
	0x360: 0xf2, 0x362: 0xf3, 0x363: 0xf4, 0x364: 0xf5, 0x365: 0xf6, 0x366: 0xf7, 0x367: 0xf8,
	0x368: 0xf9, 0x369: 0xfa, 0x36a: 0xfb, 0x36b: 0xfc,
	0x370: 0xfd, 0x371: 0xfe, 0x372: 0xff, 0x374: 0x100, 0x375: 0x101, 0x376: 0x102,
	0x37b: 0x103, 0x37e: 0x104,

// 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

DATA ·IEEEConst+2456(SB)/8,$0x000000019e588d52

	/* x^103488 mod p(x), x^103424 mod p(x) */
DATA ·IEEEConst+2464(SB)/8,$0x00000001415598a0
DATA ·IEEEConst+2472(SB)/8,$0x00000001180f0bbc

	/* x^102464 mod p(x), x^102400 mod p(x) */
DATA ·IEEEConst+2480(SB)/8,$0x00000000bf6c8c90
DATA ·IEEEConst+2488(SB)/8,$0x00000000e1d9177a

	/* x^101440 mod p(x), x^101376 mod p(x) */
DATA ·IEEEConst+2496(SB)/8,$0x00000001788b0504

  // 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 =

	0x97, 0xff, 0x5d, 0x38, 0x64, 0x08, 0x00, 0x10,
	0xbc, 0x85, 0xaf, 0xdf, 0xff, 0xff, 0x7b, 0x35,
	0x3e, 0xc7, 0xc7, 0xdf, 0xff, 0x01, 0x81, 0x00,
	0xb0, 0x05, 0x80, 0x00, 0x20, 0x00, 0x00, 0x03,
	0x40, 0x00, 0x40, 0x92, 0x21, 0x50, 0xb1, 0x5d,
	// Entry 100 - 13F
	0xfd, 0xdc, 0xbe, 0x5e, 0x00, 0x00, 0x02, 0x64,
	0x0d, 0x19, 0x41, 0xdf, 0x79, 0x22, 0x00, 0x00,
	0x00, 0x5e, 0x64, 0xdc, 0x24, 0xe5, 0xd9, 0xe3,