func.func @xla_gather_known_output_shape(%arg0: tensor<5xi32>, %arg1: tensor<1xi32>, %arg2: tensor<1xi32>) -> tensor<i32> {
  // dimension_numbers: {
  //   collapsed_slice_dims: 0
  //   start_index_map: 0
  // }
  %0 = "tf.XlaGather"(%arg0, %arg1, %arg2) {device = "", dimension_numbers = "\12\01\00\1A\01\00", indices_are_sorted = true} : (tensor<5xi32>, tensor<1xi32>, tensor<1xi32>) -> tensor<i32>

// CHECK-DAG:   [[ZERO_1:%cst.*]] = arith.constant dense<0> : tensor<i32>
// CHECK-DAG:   [[PARTIAL_INDEX:%.*]] = "tf.Fill"([[VECTOR_RANK]], [[ZERO_1]]) : (tensor<1xi32>, tensor<i32>) -> tensor<?xi32>
// CHECK-DAG:   [[EXPANDED_INDIX:%.*]] = "tf.ExpandDims"([[INDEX]], [[ZERO]]) : (tensor<i32>, tensor<i32>) -> tensor<1xi32>

// RUN: tf-opt %s -tfl-legalize-tf='preserve-assert-op=true' | FileCheck %s --dump-input=fail

func.func @preserve_assert(%arg0: tensor<1xi32>, %arg1: tensor<1xi32>) -> tensor<1xi1> {
  %0 = "tf.LessEqual"(%arg0, %arg1) : (tensor<1xi32>, tensor<1xi32>) -> tensor<1xi1>
  "tf.Assert"(%0, %arg1) {summarize = 3} : (tensor<1xi1>, tensor<1xi32>) -> ()
  func.return %0 : tensor<1xi1>
  // CHECK-LABEL: preserve_assert
  // CHECK: tfl.less_equal
  // CHECK: Assert

    %elem_shape_2 = "tf.Const"() {value = dense<-1> : tensor<1xi32>} : () -> tensor<1xi32>
    %tls = "tf.TensorListStack"(%tl_set_item, %elem_shape_2) {num_elements = 300 : i64} : (tensor<!tf_type.variant<tensor<*xf32>>>, tensor<1xi32>) -> tensor<300x?xf32>

    %elem_2 = "tf.Const"() {value = dense<10.0> : tensor<9xf32>} : () -> tensor<9xf32>

  %cst_0 = "tf.Const"() {value = dense<1> : tensor<1xi32>} : () -> tensor<1xi32>
  %cst_1 = "tf.Const"() {value = dense<2> : tensor<1xi32>} : () -> tensor<1xi32>
  %cst_2 = "tf.Const"() {value = dense<3> : tensor<1xi32>} : () -> tensor<1xi32>
  %cst_3 = "tf.Const"() {value = dense<4> : tensor<1xi32>} : () -> tensor<1xi32>

    func.func @main() -> tensor<1xi32> {
      %0 = "tf.BadValue"() {value = dense<1000> : tensor<1xi32>} : () -> tensor<1xi32>
      %1 = "tf.AlsoBad"() {value = dense<10> : tensor<1xi32>} : () -> tensor<1xi32>
      func.return %0 : tensor<1xi32>
    }
  })";
  CellReader<int64_t> error(kFailedLegalizationStreamz);
  CreateModule(kMultipleFailures);

func.func @main() -> (tensor<1xi32>, tensor<1xi32>) {
  %0, %1 = tf_executor.graph {
    %output_1, %token_1, %control_1 = tf_executor.NextIteration.Source : tensor<1xi32> {T = i32, device = ""}
    %output_2, %token_2, %control_2 = tf_executor.NextIteration.Source : tensor<1xi32> {T = i32, device = ""}
    tf_executor.NextIteration.Sink [%token_1] %output_1 : tensor<1xi32> {T = i32, device = ""}

    %4 = "tf.LayerNorm"(%a1, %a2, %a3, %a4) {_tfl_quant_trait = "fully_quantizable", device = ""} : (tensor<128x128xf32>, tensor<1xf32>, tensor<1xf32>, tensor<1xi32>) -> tensor<128x128xf32>
   "tfl.yield"(%4) : (tensor<128x128xf32>) -> ()
  }) {_tfl_quant_trait = "fully_quantizable", device = ""} : (tensor<128x128xf32>, tensor<1xf32>, tensor<1xf32>, tensor<1xi32>) -> tensor<128x128xf32>

  module attributes {tf.versions = {bad_consumers = [], min_consumer = 0 : i32, producer = 268 : i32}} {
    func.func @main() -> tensor<1xi32> {
      %0 = "tf.Const"() {value = dense<1000> : tensor<1xi32>} : () -> tensor<1xi32>
      return %0 : tensor<1xi32>
    }
  })";
  CreateModule(kMlirModuleStr);

  auto result = Run();

  EXPECT_TRUE(result.succeeded());
}

  func.return %1 : tensor<1x1x257xi32>
  // CHECK: %[[CONST_DIM:.*]] = "tfl.pseudo_const"() <{value = dense<1> : tensor<1xi32>}> : () -> tensor<1xi32>
  // CHECK: %[[RED:.*]] = "tfl.sum"(%arg0, %[[CONST_DIM]]) <{keep_dims = true}> : (tensor<1x16384x257xi32>, tensor<1xi32>) -> tensor<1x1x257xi32>
}

// CHECK-LABEL: BatchmatmulToReduceSumF32
// CHECK-NOT: "tfl.batch_matmul"