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: mlir_module = "\0Afunc @main(%arg0 : tensor<10xf32>, %arg1 : tensor<10xf32>) -> tensor<10x10xf32> {\0A %add = \22tf.Add\22(%arg0, %arg1) : (tensor<10xf32>, tensor<10xf32>) -> tensor<10xf32>\0A %ret = \22magic.op\22(%add, %add) : (tensor<10xf32>, tensor<10xf32>) -> tensor<10x10xf32>\0A return %ret : tensor<10x10xf32>\0A}\0A"}> {device = ""} : (tensor<10xf32>, tensor<10xf32>) -> tensor<*xf32>

node {
  name: "x"
  op: "Placeholder"
  attr {

module {
  // CHECK-LABEL: func @main
  // CHECK-SAME: %[[ARG_0:.*]]: tensor<10xf32>, %[[ARG_1:.*]]: tensor<10xf32>, %[[ARG_2:.*]]: tensor<6xi32>
  func.func @main(
      %arg0: tensor<10xf32>, %arg1: tensor<10xf32>, %arg2: tensor<6xi32>
    ) -> (tensor<10xf32>, tensor<6xi32>) {
    // CHECK: %[[CAST_0:.*]] = "tf.Cast"(%[[ARG_0]]) <{Truncate = false}> : (tensor<10xf32>) -> tensor<10xbf16>

  attributes {tf_saved_model.exported_names = ["assign_from_global"]} {
    // CHECK: [[name:%.*]] = "tf.VarHandleOp"
    // CHECK: "tf.AssignVariableOp"([[name]]
    %0 = "tf.Const"() {type = tensor<10xf32>, value = dense<[0.,10.,2.,3.,4.,5.,6.,7.,8.,9.]> : tensor<10xf32>} : () -> tensor<10xf32>
    "tf.AssignVariableOp"(%arg0, %0) : (tensor<!tf_type.resource<tensor<10xf32>>>, tensor<10xf32>) -> ()

    %0 = stablehlo.constant dense<500> : tensor<100xi64>
    %1 = stablehlo.constant dense<0> : tensor<100xi64>
    %2 = stablehlo.compare  LT, %arg2, %1 : (tensor<100xi64>, tensor<100xi64>) -> tensor<100xi1>
    %3 = stablehlo.add %arg2, %0 : tensor<100xi64>
    %4 = stablehlo.select %2, %3, %arg2 : tensor<100xi1>, tensor<100xi64>
    %5 = stablehlo.reshape %4 : (tensor<100xi64>) -> tensor<100x1xi64>

  func.return %0 : tensor<10xf32>
}

func.func @main(%arg0: tensor<7xf32>) -> tensor<10xf32> attributes {tf.entry_function = {control_outputs = "", inputs = "input:0", outputs = "output:0"}} {

  %0 = "tf.TensorListFromTensor"(%arg0, %arg1) : (tensor<3x10xf32>, tensor<1xi32>) -> tensor<!tf_type.variant<tensor<10xf32>>>
  %1 = "tf.TensorListPushBack"(%0, %arg2) : (tensor<!tf_type.variant<tensor<10xf32>>>, tensor<10xf32>) -> tensor<!tf_type.variant<tensor<10xf32>>>
  %2 = "tf.TensorListStack"(%1, %arg1) : (tensor<!tf_type.variant<tensor<10xf32>>>, tensor<1xi32>) -> tensor<?x10xf32>

  // CHECK-NEXT: %[[ADDN:.*]] = "tf.AddN"(%[[UPDATE]], %[[BROADCAST]]) : (tensor<10xf32>, tensor<10xf32>) -> tensor<10xf32>
  %addn = "tf.AddN"(%set, %tl) : (tensor<!tf_type.variant<tensor<f32>>>, tensor<!tf_type.variant<tensor<f32>>>) -> tensor<!tf_type.variant<tensor<f32>>>
  // CHECK-NEXT: %[[ZEROS_LIKE:.*]] = "tf.ZerosLike"(%[[ADDN]]) : (tensor<10xf32>) -> tensor<10xf32>

module{
  func.func @main(%arg0: tensor<10xf32>) -> tensor<10xf32> {
    return %arg0 : tensor<10xf32>
  }
  func.func @func1(%arg0: tensor<10xf32>, %arg1: tensor<10xf32>) -> tensor<10xf32> {
    %0 = stablehlo.add %arg0, %arg1 : tensor<10xf32>
    %1 = stablehlo.add %arg0, %arg1 : tensor<10xf32>
    return %0 : tensor<10xf32>
  }
})mlir";

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