%9 = "tfl.fully_connected"(%8, %cst_10, %cst_0) {fused_activation_function = "NONE", keep_num_dims = false, weights_format = "DEFAULT"} : (tensor<4x5xf32>, tensor<8x5xf32>, tensor<8xf32>) -> tensor<4x8xf32>
    %10:4 = "tfl.split"(%cst_5, %9) {num_splits = 4 : i32} : (tensor<i32>, tensor<4x8xf32>) -> (tensor<4x2xf32>, tensor<4x2xf32>, tensor<4x2xf32>, tensor<4x2xf32>)

  %2 = "tfl.relu"(%arg0) : (tensor<1xf32>) -> tensor<1xf32>
  // CHECK: tac.device = "CPU", tac.inference_type = "FLOAT"
  %3 = "tfl.pack"(%arg0, %arg1) {axis = 0 : i32, values_count = 2 : i32} : (tensor<1xf32>, tensor<1xf32>) -> tensor<2x1xf32>
  func.return
}

func.func @notAnnotateConst(%arg0: tensor<256x32x32x3xf32>) -> tensor<256x30x30x16xf32> {
  // CHECK-NOT: tac.device tac.inference_type

}

func.func @einsum_matmul(%arg0: tensor<7x9xf32>, %arg1: tensor<9x5xf32>) -> tensor<7x5xf32> {
  %0 = "tf.Einsum"(%arg0, %arg1) {T = "tfdtype$DT_FLOAT", equation = "ae,ed->ad"}: (tensor<7x9xf32>, tensor<9x5xf32>) -> tensor<7x5xf32>
  func.return %0 : tensor<7x5xf32>
  // CHECK-LABEL: einsum_matmul
  // CHECK: %[[v0:.*]] = "tf.BatchMatMulV2"(%arg0, %arg1) <{adj_x = false, adj_y = false}> : (tensor<7x9xf32>, tensor<9x5xf32>) -> tensor<7x5xf32>

  func.return %3 : tensor<2x1xf32>
}
}

// CHECK:   func @simpleTest(%[[VAL_0:.*]]: tensor<1xf32>, %[[VAL_1:.*]]: tensor<1xf32>, %[[VAL_2:.*]]: tensor<1xf32>, %[[VAL_3:.*]]: tensor<1xf32>) -> tensor<2x1xf32> {

// CHECK:           %[[VAL_2:.*]] = "tf.Div"(%[[VAL_0]], %[[VAL_0]]) : (tensor<2xf32>, tensor<2xf32>) -> tensor<2xf32>
// CHECK:           %[[VAL_3:.*]] = "tf.FloorDiv"(%[[VAL_0]], %[[VAL_0]]) : (tensor<2xf32>, tensor<2xf32>) -> tensor<2xf32>
// CHECK:           return %[[VAL_3]] : tensor<2xf32>
// CHECK:         }
func.func @floordiv_f32(%arg0: tensor<2xf32>) -> tensor<2xf32> {

  // CHECK: return %[[output]] : tensor<6x3xi8>
  %0 = "tf.ConcatV2"(%arg0, %arg1, %axis) : (tensor<3x3xf32>, tensor<3x3xf32>, tensor<i64>) -> tensor<6x3xf32>
  %1 = "tf.UniformQuantize"(%0, %scales, %zps) {
    quantization_axis = -1 : i64, quantization_min_val = -128 : i64, quantization_max_val = 127 : i64
  } : (tensor<6x3xf32>, tensor<f32>, tensor<i32>) -> tensor<6x3x!tf_type.qint8>
  func.return %1 : tensor<6x3x!tf_type.qint8>
}

    ```mlir
      %0 = "tf.Const"() {value = dense<[[42.0]]> : tensor<1x1xf32>} : () -> tensor<1x1xf32>
      %1 = "tf.Const"() {device = "", value = dense<[[42.0]]> : tensor<1x1xf32>} : () -> tensor<1x1xf32>
      %2 = "tf.Const"() {device = "baz", value = dense<[[42.0]]> : tensor<1x1xf32>} : () -> tensor<1x1xf32>
    ```

    then running this pass with 'default-device=foobar', we get:

    ```mlir

For example, if we have the code

```mlir
  %0 = "tf.Const"() {value = dense<[[42.0]]> : tensor<1x1xf32>} : () -> tensor<1x1xf32>
  %1 = "tf.Const"() {device = "", value = dense<[[42.0]]> : tensor<1x1xf32>} : () -> tensor<1x1xf32>
  %2 = "tf.Const"() {device = "baz", value = dense<[[42.0]]> : tensor<1x1xf32>} : () -> tensor<1x1xf32>
```

then running this pass with 'default-device=foobar', we get:

```mlir

    %2:2 = "tf.RecvTPUEmbeddingActivations"() {_tpu_embedding_layer = "call1", config = "\0A\0B\0C\0D"} : () -> (tensor<2x2xf32>, tensor<4x4xf32>)
    "tf.SendTPUEmbeddingGradients"(%2#0, %2#1) {_tpu_embedding_layer = "call1", config = "\0A\0B\0C\0D", operandSegmentSizes = array<i32: 2, 0>} : (tensor<2x2xf32>, tensor<4x4xf32>) -> ()
    tf_device.return

  func.return %24 : tensor<1x4xf32>
}