// CHECK-LABEL: roll_non_constant_shift
  // CHECK:  "tf.Roll"
}

func.func @scatter_nd_updates(%arg0: tensor<14xf32>, %arg1: tensor<1x1xi32>, %arg2: tensor<1xf32>) -> tensor<14xf32> {
  %0 = "tf.TensorScatterUpdate"(%arg0, %arg1, %arg2) : (tensor<14xf32>, tensor<1x1xi32>, tensor<1xf32>) -> tensor<14xf32>
  func.return %0 : tensor<14xf32>

  // CHECK-LABEL: scatter_nd_updates

  %max_size = "tf.Const"() {value = dense<10> : tensor<i32>} : () -> tensor<i32>
  // CHECK-NOT: tf.EmptyTensorList
  // CHECK: tf.BroadcastTo
  // CHECK-SAME: tensor<10x16xf32>
  %tl0 = "tf.EmptyTensorList"(%element_shape, %max_size) : (tensor<1xi32>, tensor<i32>) -> tensor<!tf_type.variant<tensor<16xf32>>>
  // CHECK-NOT: tf.TensorListReserve
  // CHECK: tf.BroadcastTo

// CHECK:   [[ZERO:%.*]] = arith.constant dense<0> : tensor<i32>
// CHECK:   [[EXP_ITEM:%.*]] = "tf.ExpandDims"([[ITEM]], [[ZERO]]) {{.*}} -> tensor<1x10xf32>
// CHECK:   [[RESULT:%.*]] = "tf.Concat"(%cst, [[INPUT]], [[EXP_ITEM]]) : {{.*}} -> tensor<?x10xf32>
// CHECK:   return [[RESULT]] : tensor<?x10xf32>
}

// -----

  // expected-error @+1 {{'tfl.prelu' op result type '10x10' not broadcast compatible with broadcasted operands's shapes '10x10x10x10'}}
  %0 = "tfl.prelu"(%arg0, %arg1) : (tensor<10x10x10x10xf32>, tensor<10x10x10x10xf32>) -> tensor<10x10xf32>
  func.return %0 : tensor<10x10xf32>
}

// -----

  %19 = "tf.StringLength"(%arg0) {device = "", unit = "BYTE"} : (tensor<1x!tf_type.string>) -> tensor<1xi32>
  %20 = "tf.ExpandDims"(%19, %7) {device = ""} : (tensor<1xi32>, tensor<i32>) -> tensor<1x1xi32>
  %21 = "tf.Cast"(%20) {Truncate = false, device = ""} : (tensor<1x1xi32>) -> tensor<1x1xi64>
  %22 = "tf.Reshape"(%21, %12) {device = ""} : (tensor<1x1xi64>, tensor<1xi64>) -> tensor<1xi64>

    }
  }
  return new_arg_type;
}

// Combination of value producer and port of value produced (e.g.,
//   <value result output>:<value in output tensor>,
// so for tf.Const -> tensor<10x20xf32>, [0,2,18] would point to a unique output
// scalar value).
struct ValuePort {
  PointerUnion<Operation*, BlockArgument> producer;
  SmallVector<unsigned int, 2> port;

mlir_module = '''python
func @main(%arg0 : tensor<10xf32>, %arg1 : tensor<10xf32>) -> tensor<10x10xf32> {
   %add = "magic.op"(%arg0, %arg1) : (tensor<10xf32>, tensor<10xf32>) -> tensor<10x10xf32>
   return %ret : tensor<10x10xf32>
}
'''

@tf.function
def foo(x, y):
  return mlir_passthrough_op([x, y], mlir_module, Toutputs=[tf.float32])