unknown_rank: true
      }
    }
  }
}
versions {
  producer: 216
}

# CHECK-NO-SHAPE: func @main(%arg0: tensor<i32>, %arg1: tensor<!tf_type.resource<tensor<i32>>>) -> (tensor<i32>, tensor<!tf_type.resource<tensor<i32>>>)

// CHECK-LABEL: @add_f32(%arg0: tensor<3x3xbf16>, %arg1: tensor<3x3xbf16>) -> tensor<3x3xbf16>
func.func @add_f32(%arg0: tensor<3x3xf32>, %arg1: tensor<3x3xf32>) -> tensor<3x3xf32> {
  // CHECK-NOT: f32
  // CHECK: stablehlo.add
  %0 = stablehlo.add %arg0, %arg1: (tensor<3x3xf32>, tensor<3x3xf32>) -> tensor<3x3xf32>
  return %0 : tensor<3x3xf32>
}

// -----

// CHECK-LABEL: func @convert_to_legacy_attribute
func.func @convert_to_legacy_attribute(%arg0: tensor<*xf32>, %arg1: tensor<f32>, %arg2: tensor<f32>) -> tensor<*xf32> attributes {tf._disable_call_shape_inference = true, tf.signature.is_stateful} {
  // CHECK-NOT: _replication_info =
  %0 = tf_executor.graph {
    %outputs, %control = tf_executor.island wraps "tf.GuaranteeConst"(%arg1) {T = f32, device = ""} : (tensor<f32>) -> tensor<f32>

# Verify main graph was converted to a function, args/rets are mapped correctly,
# and ops in the main graph are retained. In addition, check if subsequent
# functions are converted.

# CHECK:      func @main(%arg0: tensor<*x!tf_type.resource>, %arg1: tensor<*x!tf_type.resource<tensor<3x3x1x32xf32>>>, %arg2: tensor<*xf32>, %arg3: tensor<2x4x6x8xi32>) -> (tensor<*xf32>, tensor<*xf32>)

// RUN: flatbuffer_translate -mlir-to-tflite-flatbuffer %s -o - | flatbuffer_to_string - | FileCheck %s

func.func @main(%arg0: tensor<4xi32>, %arg1: tensor<32x4x4x128xf32>, %arg2: tensor<1x32x42x128xf32>) -> tensor<1x64x84x32xf32> {
// CHECK: {
// CHECK-NEXT:  version: 3,
// CHECK-NEXT:  operator_codes: [ {
// CHECK-NEXT:    deprecated_builtin_code: 67,
// CHECK-NEXT:    version: 1,
// CHECK-NEXT:    builtin_code: TRANSPOSE_CONV
// CHECK-NEXT:  } ],

// CHECK: %[[id2:.*]] = "tf.RiscSame"(%arg2) : (tensor<f32>) -> tensor<*xf32>
// CHECK: %[[es:.*]] = "tf.EnsureShape"(%[[id2]]) <{shape = #tf_type.shape<>}> : (tensor<*xf32>) -> tensor<f32>
// CHECK: return %[[es]] : tensor<f32>
}

// CHECK-LABEL: decompose_tf_concat_n
func.func @decompose_tf_concat_n(%arg0: tensor<f32>, %arg1: tensor<f32>, %arg2: tensor<f32>) -> tensor<3xf32> {
  %0 = "tfr.cast"(%arg0) : (tensor<f32>) -> !tfr.tensor

// RUN: tf-mlir-translate -mlir-tf-to-hlo-text %s -tf-input-shapes=128,10:10,1024:128,1024 -tf-xla-emit-use-tuple-args -tf-xla-emit-return-tuple | FileCheck %s

module attributes {tf.versions = {bad_consumers = [], min_consumer = 0 : i32, producer = 351 : i32}} {

  func.func private @if_then_branch(%arg0: tensor<*xi1>, %arg1: tensor<*xi64>, %arg2: tensor<*xi64>) -> tensor<*xi1> {
    func.return %arg0 : tensor<*xi1>
  }
  func.func private @if_else_branch(%arg0: tensor<*xi1>, %arg1: tensor<*xi64>, %arg2: tensor<*xi64>) -> tensor<*xi1> {
    func.return %arg0 : tensor<*xi1>
  }
  func.func @"tpu_subgraph"(%arg0: tensor<*xi64>) -> tensor<*xi1> {

  // CHECK: func @f(%arg0: tensor<!tf_type.resource<tensor<f32>>>  {tf_saved_model.index_path = [0]})
  func.func @f(%arg0: tensor<!tf_type.resource<tensor<f32>>>  {tf_saved_model.index_path = [0]})
  attributes {tf_saved_model.exported_names = ["f"]} {
    %val = "tf.ReadVariableOp"(%arg0) : (tensor<!tf_type.resource<tensor<f32>>>) -> tensor<f32>

  // CHECK: func @__concrete_function_run_computation
  func.func @__concrete_function_run_computation(
    %arg0: tensor<f32> {tf_saved_model.index_path = [0, "foo"]},
    %arg1: tensor<!tf_type.resource<tensor<1x64xf32>>> {tf_saved_model.bound_input = @some_constant},
    %arg2: tensor<!tf_type.resource<tensor<?x64xf32>>> {tf_saved_model.bound_input = @some_variable}
  ) -> (