func.func @random_uniform_int(%arg0: tensor<i32>, %arg1: tensor<i32>) -> tensor<1000xi32> {
    %0 = "tf.Const"() {value = dense<1000> : tensor<1xi32>} : () -> tensor<1xi32>
    // CHECK-NOT: tf.RandomUniformInt
    %1 = "tf.RandomUniformInt"(%0, %arg0, %arg1) {seed = 0 : i64, seed2 = 0 : i64} : (tensor<1xi32>, tensor<i32>, tensor<i32>) -> tensor<1000xi32>
    func.return %1 : tensor<1000xi32>
  }

  // CHECK-LABEL: multinomial

      /*error_tolerance=*/1.0);
}

TEST_F(ConvertTfQuantToMhloIntTest, UniformQuantizeAddToValidGraph) {
  constexpr absl::string_view kProgram = R"mlir(
func.func @main(%lhs: tensor<10x10xi32>, %rhs: tensor<10x10xi32>) -> tensor<10x10xi32> {
  %lhs_scale = "tf.Const"() { value = dense<0.518> : tensor<f32> } : () -> tensor<f32>
  %lhs_zp = "tf.Const"() { value = dense<42> : tensor<i32> } : () -> tensor<i32>

// CHECK:           [[VAL_5:%.*]] = call @func_1_GPU_FLOAT([[VAL_0]], [[VAL_3]]) {tac.device = "GPU", tac.inference_type = "FLOAT", tac.interface_name = "func_1"} : (tensor<100xf32>, tensor<100xf32>) -> tensor<100xf32>

  %3 = "tf.PreventGradient"(%1) : (tensor<10x10xi32>) -> tensor<10x10xi32>
  %4 = "tf.AddV2"(%2, %3) {device = ""} : (tensor<10x10xi32>, tensor<10x10xi32>) -> tensor<10x10xi32>
  func.return %4 : tensor<10x10xi32>

// CHECK-LABEL: preventGradient
// CHECK-NOT: %2 = "tf.PreventGradient"(%0) : (tensor<10x10xi32>) -> tensor<10x10xi32>

  %0 = "mhlo.broadcast_in_dim"(%arg1) <{broadcast_dimensions = dense<[0, 1]> : tensor<2xi64>}> : (tensor<1x1xi32>) -> tensor<1x100xi32>
  %1 = "mhlo.select"(%arg0, %0, %arg2) : (tensor<i1>, tensor<1x100xi32>, tensor<1x100xi32>) -> tensor<1x100xi32>
  func.return %1 : tensor<1x100xi32>
}

// CHECK-LABEL:   func @selectv2_broadcasted_condition(

    "vhlo.return_v1"(%lhs) : (tensor<f32>) -> ()
  }): (tensor<200x100x300xf32>, tensor<10x2xi32>, tensor<10x300xf32>) ->
      tensor<200x100x300xf32>
  func.return %0 : tensor<200x100x300xf32>
}

// CHECK-LABEL: func.func private @scatter(%arg0: tensor<200x100x300xf32>, %arg1: tensor<10x2xi32>, %arg2: tensor<10x300xf32>) -> tensor<200x100x300xf32> {

// CHECK-LABEL: func @func_with_call
func.func private @some_func(%arg0: tensor<100xf32>) -> tensor<100xf32> attributes {tf.api_implements = "lstm_b4e9f0e7-ac55-42bc-8ef2-8496419a608c"}
func.func @func_with_call(%arg0: tensor<100xf32>) -> tensor<100xf32> {
  %0 = func.call @some_func(%arg0) : (tensor<100xf32>) -> tensor<100xf32>
  func.return %0 : tensor<100xf32>
  }
}

// -----

module {

	{"2001:db8::1/-1", nil, nil, &ParseError{Type: "CIDR address", Text: "2001:db8::1/-1"}},
	{"2001:db8::1/-0", nil, nil, &ParseError{Type: "CIDR address", Text: "2001:db8::1/-0"}},
	{"-0.0.0.0/32", nil, nil, &ParseError{Type: "CIDR address", Text: "-0.0.0.0/32"}},
	{"0.-1.0.0/32", nil, nil, &ParseError{Type: "CIDR address", Text: "0.-1.0.0/32"}},

  %cst = arith.constant dense<[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]> : tensor<10xi32>
  %1 = "tfl.equal"(%arg0, %cst) : (tensor<1x?xi32>, tensor<10xi32>) -> tensor<1x?x10xi1>
  %2 = "tfl.cast"(%1) : (tensor<1x?x10xi1>) -> tensor<1x?x10xf32>
  func.return %2 : tensor<1x?x10xf32>

  // CHECK-DAG: %[[CST:.*]] = arith.constant dense<[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]> : tensor<10xi32>

  func.return %0 : tensor<1x300x10xf32>
}

// -----

// CHECK-LABEL: @xla_gather_i32
func.func @xla_gather_i32(%arg0: tensor<200x100x300xf32>, %arg1: tensor<10x2xi32>) -> tensor<1x300x10xf32> {