%s0 = "tf.Const"() {value = dense<[501, 1, 32, 1280]> : tensor<4xi32>} : () -> tensor<4xi32>
    %s1 = "tf.Const"() {value = dense<[  1, 1,  1, 1280]> : tensor<4xi32>} : () -> tensor<4xi32>

    // CHECK: "tf.BroadcastGradientArgs"
    // CHECK-NOT: _xla_outside_compilation
    %r0, %r1 = "tf.BroadcastGradientArgs"(%s0, %s1) {} : (tensor<4xi32>, tensor<4xi32>) -> (tensor<1xi32>, tensor<3xi32>)
    tf_device.return

  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>) -> ()

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-NEXT:      %18 = "tfl.pseudo_const"() <{value = dense<2> : tensor<1xi32>}> : () -> tensor<1xi32>
// CHECK-NEXT:      %19 = "tfl.concatenation"(%18, %17, %16) <{axis = 0 : i32, fused_activation_function = "NONE"}> : (tensor<1xi32>, tensor<1xi32>, tensor<1xi32>) -> tensor<3xi32>
// CHECK-NEXT:      %20 = mhlo.dynamic_reshape %2, %19 : (tensor<2x3x4x?xf32>, tensor<3xi32>) -> tensor<2x3x?xf32>

    } : (tensor<10xf32>, tensor<10xf32>, tensor<6xi32>) -> (tensor<10xf32>, tensor<6xi32>)
    // CHECK: return %[[RESULT_CAST]], %[[RESULT]]#1 : tensor<10xf32>, tensor<6xi32>
    func.return %0#0, %0#1 : tensor<10xf32>, tensor<6xi32>
  }

  // CHECK-LABEL: func private @main_0
  // CHECK-SAME: %[[ARG_0:.*]]: tensor<10xbf16>, %[[ARG_1:.*]]: tensor<10xbf16>, %[[ARG_2:.*]]: tensor<6xi32>
  func.func private @main_0(

# 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 {

    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);

    %2 = stablehlo.reshape %1 : (tensor<i32>) -> tensor<1xi32>
    %3 = stablehlo.concatenate %2, %cst_0, %cst_1, %cst_2, dim = 0 : (tensor<1xi32>, tensor<1xi32>, tensor<1xi32>, tensor<1xi32>) -> tensor<4xi32>
    %4 = stablehlo.dynamic_broadcast_in_dim %arg2, %3, dims = [0, 1, 2, 3] : (tensor<1x1x1x2xf32>, tensor<4xi32>) -> tensor<?x3x4x2xf32>
    %5 = stablehlo.add %0, %4 : tensor<?x3x4x2xf32>

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: "tf.Cast"(%arg0) <{Truncate = false}> : (tensor<1xi32>) -> tensor<1xi32>
  %0 = "tf.Cast"(%arg0) {Truncate = false} : (tensor<1x!tf_type.qint32>) -> tensor<1xi32>
  func.return %0: tensor<1xi32>
}

// -----

// CHECK-LABEL: func @cast_op_int32_qint32
func.func @cast_op_int32_qint32(%arg0: tensor<1xi32>) -> tensor<1x!tf_type.qint32> {