%2 = "tf.Reshape"(%1, %cst_0) : (tensor<1x10xf32>, tensor<2xi32>) -> tensor<?x10xf32> loc(fused["Reshape:", "model/reshape"])
    %3 = "tf.AddV2"(%2, %cst) : (tensor<?x10xf32>, tensor<10xf32>) -> tensor<?x10xf32> loc(fused["AddV2:", "model/add"])
    func.return %3 : tensor<?x10xf32>
  }
}

// CHECK-LABEL: func @matmul_with_reshape_disable_quantization
// CHECK: "tf.PartitionedCall"

      tf.TensorSpec([], tf.float32)
  ])
  def some_function(self, x, y):
    return x + y
    # Basic check that the debug info file is being correctly saved and loaded.
    #
    # CHECK: "tf.AddV2"{{.*}}loc(#loc{{[0-9]+}})
    # CHECK: "tf.Identity"{{.*}}loc(#loc{{[0-9]+}})
    # CHECK: #loc{{[0-9]+}} = loc("{{.*}}debug_info.py":{{[0-9]+}}:{{[0-9]+}})
    # CHECK: #loc{{[0-9]+}} = loc(callsite(#loc{{[0-9]+}} at #loc{{[0-9]+}}))

// CHECK-LABEL: func @createop
func.func @createop(%in_ch: !tfrt.chain) -> !tfrt.chain {

  // CHECK: [[ch:%.*]] = tfrt_fallback_async.createop(%{{.*}}) key(100) device("cpu") "tf.AddV2"() {T = i32} num_args(2)
  %out_ch = tfrt_fallback_async.createop(%in_ch) key(100) device("cpu") "tf.AddV2"() {T = i32} num_args(2)

  // CHECK: tfrt.return [[ch]]
  tfrt.return %out_ch: !tfrt.chain
}

// CHECK-LABEL: func @fallback_resource

    %val = "tf.ReadVariableOp"(%handle) : (tensor<!tf_type.resource<tensor<0xf32>>>) -> tensor<0xf32>
    %0 = "tf.AddV2"(%val, %val) : (tensor<0xf32>, tensor<0xf32>) -> tensor<0xf32>
    "tf.AssignVariableOp"(%handle, %0) : (tensor<!tf_type.resource<tensor<0xf32>>>, tensor<0xf32>) -> ()
    // CHECK: "tf.VarHandleOp"
    func.return
  }
}

  %0 = "tf.ReadVariableOp"(%arg1) {device = "/device:CPU:0"} : (tensor<*x!tf_type.resource>) -> tensor<1x3xf32>
  %1 = "tf.AddV2"(%arg0, %0) {device = "/device:CPU:0"} : (tensor<1x3xf32>, tensor<1x3xf32>) -> tensor<1x3xf32>
  %2 = "tf.Identity"(%1) {device = "/device:CPU:0"} : (tensor<1x3xf32>) -> tensor<1x3xf32>
  func.return %2 : tensor<1x3xf32>
}

  return op_ptr->Execute(absl::MakeSpan(output, 1), &num_retvals);
}

// Op: AddV2()
// Summary: Returns x + y element-wise.
//
// Description:
//   *NOTE*: `Add` supports broadcasting. `AddN` does not. More about
//   broadcasting
//   [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html)
Status AddV2(AbstractContext* ctx, AbstractTensorHandle* const x,

func Or8(ptr *uint8, val uint8)

// NOTE: Do not add atomicxor8 (XOR is not idempotent).

//go:noescape
func And(ptr *uint32, val uint32)

//go:noescape
func Or(ptr *uint32, val uint32)

//go:noescape
func And32(ptr *uint32, val uint32) uint32

//go:noescape
func Or32(ptr *uint32, val uint32) uint32

//go:noescape
func And64(ptr *uint64, val uint64) uint64

//go:noescape
func Or64(ptr *uint64, val uint64) uint64

func Or8(ptr *uint8, val uint8)

//go:noescape
func And8(ptr *uint8, val uint8)

//go:noescape
func And(ptr *uint32, val uint32)

//go:noescape
func Or(ptr *uint32, val uint32)

//go:noescape
func And32(ptr *uint32, val uint32) uint32

//go:noescape
func Or32(ptr *uint32, val uint32) uint32

//go:noescape
func And64(ptr *uint64, val uint64) uint64

//go:noescape
func Or64(ptr *uint64, val uint64) uint64

    %outputs_13, %control_14 = tf_executor.island wraps "tf.AddV2"(%outputs_11, %outputs_0) {_replication_info = "cluster", _xla_compile_device_type = "TPU", device = ""} : (tensor<*xf32>, tensor<f32>) -> tensor<*xf32>

  %cst = arith.constant dense<[5, 7]> : tensor<2xi32>
  %0 = "tf.BroadcastTo"(%arg1, %cst) : (tensor<7xf32>, tensor<2xi32>) -> tensor<5x7xf32>
  %1 = "tf.AddV2"(%arg0, %0) : (tensor<5x1xf32>, tensor<5x7xf32>) -> tensor<5x7xf32>
  func.return %1 : tensor<5x7xf32>
  // CHECK: %[[V0:.*]] = "tf.AddV2"(%arg0, %arg1) : (tensor<5x1xf32>, tensor<7xf32>) -> tensor<5x7xf32>
  // CHECK: %[[V0]] : tensor<5x7xf32>
}

// CHECK-LABEL: @broadcast_mul_implicit_no_fold