module attributes {tf.versions = {bad_consumers = [], min_consumer = 0 : i32, producer = 268 : i32}} {
    func.func @main() -> () {
      %cst0 = "tf.Const"(){ value = dense<0> : tensor<3x5xi1>} : () -> tensor<3x5xi1>
      %0 = "tf.Where"(%cst0) : (tensor<3x5xi1>) -> tensor<?x2xi64>
      func.return
    }
  })";

  CellReader<int64_t> dynamism_counter(kDynamismOpCounterStreamzName);
  CellReader<int64_t> dynamism_function_counter(

  module attributes {tf.versions = {bad_consumers = [], min_consumer = 0 : i32, producer = 268 : i32}} {
  func.func @main() -> () {
    %cst0 = "tf.Const"(){ value = dense<0> : tensor<3x5xi1>} : () -> tensor<3x5xi1>
    %0 = "tf.Where"(%cst0) : (tensor<3x5xi1>) -> tensor<?x2xi64>
    func.return
  }
})";

absl::StatusOr<XlaCompiler::CompilationResult> CompileMlirModule(
    const char* mlir_module_str,

}

func.func @concat_v2_with_bool_type(%arg0: tensor<?x1xi1>, %arg1: tensor<?x1xi1>) -> tensor<?x2xi1> {
  %0 = "tf.Const"() { value = dense<-1> : tensor<i32> } : () -> tensor<i32>
  %1 = "tf.ConcatV2"(%arg0, %arg1, %0) : (tensor<?x1xi1>, tensor<?x1xi1>, tensor<i32>) -> tensor<?x2xi1>
  func.return %1 : tensor<?x2xi1>

// CHECK-LABEL: concat_v2_with_bool_type

  func.return %0 : tensor<3x4xi1>
}

// CHECK-LABEL: @is_nan
func.func @is_nan(%arg0: tensor<3x4xf32>) -> tensor<3x4xi1> {
  // CHECK: %[[RESULT:.*]] = "tf.NotEqual"(%arg0, %arg0) <{incompatible_shape_error = true}> : (tensor<3x4xf32>, tensor<3x4xf32>) -> tensor<3x4xi1>
  %0 = "tf.IsNan"(%arg0) : (tensor<3x4xf32>) -> tensor<3x4xi1>
  // CHECK: return %[[RESULT]]
  func.return %0 : tensor<3x4xi1>
}

}

// CHECK-LABEL: EliminateRedundantLogicalOr
func.func @EliminateRedundantLogicalOr(%arg0: tensor<1xi1>, %arg1: tensor<2x3xi1>) -> (tensor<1xi1>, tensor<2x3xi1>, tensor<2x3xi1>, tensor<2x3xi1>, tensor<2x3xi1>, tensor<2x3xi1>, tensor<1xi1>, tensor<2x3xi1>, tensor<2x3xi1>, tensor<2x3xi1>, tensor<2x3xi1>, tensor<2x3xi1>, tensor<2x3xi1>, tensor<2x3xi1>) {
  %cst_false0 = arith.constant dense<false> : tensor<1xi1>

// CHECK-SAME:                        %[[VAL_1:.*]]: tensor<1x2xi1>) -> tensor<1x2xi1> {
// CHECK:           %[[VAL_2:.*]] = "tf.LogicalAnd"(%[[VAL_0]], %[[VAL_1]]) : (tensor<1xi1>, tensor<1x2xi1>) -> tensor<1x2xi1>
// CHECK:           return %[[VAL_2]] : tensor<1x2xi1>
// CHECK:         }
func.func @and_broadcast(%arg0: tensor<1xi1>, %arg1: tensor<1x2xi1>) -> tensor<1x2xi1> {

// CHECK-LABEL: @all_keep_dim
func.func @all_keep_dim(%input: tensor<4x8xi1>) -> tensor<4x1xi1> {
  // CHECK: mhlo.reshape %{{.*}} : (tensor<4xi1>) -> tensor<4x1xi1>
  %dims = "tf.Const"() { value = dense<1> : tensor<1xi32>} : () -> tensor<1xi32>
  %0 = "tf.All"(%input, %dims) {keep_dims = true} : (tensor<4x8xi1>, tensor<1xi32>) -> tensor<4x1xi1>
  func.return %0 : tensor<4x1xi1>
}

// -----

// CHECK-LABEL: @all_dynamic

  // CHECK-SAME: %[[ARG0:.*]]: tensor<1x2xf32>
  // CHECK-SAME: %[[ARG1:.*]]: tensor<2x3xf32>
  // CHECK-SAME: %[[ARG2:.*]]: tensor<1x3xi1>
  // CHECK-SAME: %[[ARG3:.*]]: tensor<1x3xf32>
  func.func private @composite_and_select(%arg0: tensor<1x2xf32>, %arg1: tensor<2x3xf32>, %arg2: tensor<1x3xi1>, %arg3: tensor<1x3xf32>) -> tensor<1x3xf32> {

func.func @equal_broadcast(%arg0: tensor<1xi32>, %arg1: tensor<1x2xi32>) -> tensor<1x2xi1> {
  // CHECK-DAG: %[[LHS_BCAST:.+]] = "mhlo.broadcast_in_dim"(%arg0) <{broadcast_dimensions = dense<1> : tensor<1xi64>}>
  // CHECK-NEXT: mhlo.compare EQ, %[[LHS_BCAST]], %arg1
  %0 = "tf.Equal"(%arg0, %arg1) : (tensor<1xi32>, tensor<1x2xi32>) -> tensor<1x2xi1>
  func.return %0: tensor<1x2xi1>
}

// CHECK-LABEL: func @equal_broadcast_no_incompatible_shapes_error

}

// -----

// CHECK-LABEL: func @testBitcast_v2
func.func @testBitcast_v2(%arg0: tensor<3x2xi16>) -> tensor<3xi32> {
  %0 = "tf.Bitcast"(%arg0) : (tensor<3x2xi16>) -> tensor<3xi32>
  func.return %0 : tensor<3xi32>
}

// -----

func.func @testBitcast_v3(%arg0: tensor<3x5xi16>) -> tensor<3xi32> {