// CHECK:         }
func.func @broadcast_add(%arg0: tensor<1x1xf32>, %arg1: tensor<1x1000xf32>) -> (tensor<1x1000xf32>, tensor<1x1000xf32>) {
  %0 = "mhlo.broadcast_in_dim"(%arg0) <{broadcast_dimensions = dense<[0, 1]> : tensor<2xi64>}> : (tensor<1x1xf32>) -> tensor<1x1000xf32>
  %1 = mhlo.add %0, %arg1 : tensor<1x1000xf32>
  %2 = mhlo.add %arg1, %0 : tensor<1x1000xf32>

  func.return %1 : tensor<2x1024xf32>

  // CHECK: tfl.mul
}

// CHECK-LABEL: @FuseMulWithFullyConnectedNoBias
func.func @FuseMulWithFullyConnectedNoBias(%arg: tensor<2x512xf32>) -> tensor<2x1024xf32> {
  %cst_mul = arith.constant dense<2.0> : tensor<512xf32>

func.func @infeed_dequeue_tuple() -> (tensor<1x8x4x4xi32>, tensor<1x100x1xf32>) {
// CHECK: [[TOKEN:%.*]] = mhlo.create_token  : !mhlo.token
// CHECK: [[INFEED:%.*]]:3 = "mhlo.infeed"([[TOKEN]]) <{infeed_config = ""{{.*}}}> : (!mhlo.token) -> (tensor<1x8x4x4xi32>, tensor<1x100x1xf32>, !mhlo.token)
// CHECK: return [[INFEED]]#0, [[INFEED]]#1
  %0:2 = "tf.InfeedDequeueTuple"() : () -> (tensor<1x8x4x4xi32>, tensor<1x100x1xf32>)