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

  %1 = "tf.AddV2"(%arg0, %0) : (tensor<4x1xf32>, tensor<1x2xf32>) -> tensor<4x2xf32>
  %2 = "tf.AddV2"(%0, %arg0) : (tensor<1x2xf32>, tensor<4x1xf32>) -> tensor<4x2xf32>

  // If operand has the same shape as a result, we can fold it.
  %3 = "tf.AddV2"(%arg1, %0) : (tensor<4x2xf32>, tensor<1x2xf32>) -> tensor<4x2xf32>
  %4 = "tf.AddV2"(%0, %arg1) : (tensor<1x2xf32>, tensor<4x2xf32>) -> tensor<4x2xf32>

  // CHECK: %[[CONST:.*]] = "tf.Const"()

func.func @testPackedTPUPartitionedInputV2(tensor<2x4xf32>, tensor<2x4xf32>) -> tensor<4x4xf32> {
^bb0(%arg0: tensor<2x4xf32>, %arg1: tensor<2x4xf32>):
  // expected-error @+1 {{expected 1 inputs, got 2}}
  %0 = "tf.TPUPartitionedInputV2"(%arg0, %arg1) {partition_dims = [2, 1], is_packed = true} : (tensor<2x4xf32>, tensor<2x4xf32>) -> tensor<4x4xf32>
  func.return %0 : tensor<4x4xf32>
}

// -----

// CHECK:           }) : (tensor<1x?xf32>, tensor<1x0xf32>, tensor<1x0xf32>, tensor<1x0xf32>, tensor<1x0xf32>, tensor<1x3xf32>, tensor<1x3xf32>, tensor<1x3xf32>, tensor<1x3xf32>, none, none, none, tensor<1xf32>, tensor<1xf32>, tensor<1xf32>, tensor<1xf32>, tensor<3x1xf32>, tensor<3xf32>, tensor<1x3xf32>, tensor<1x1xf32>, none, none, none, none) -> tensor<1x3xf32>
// CHECK:           [[VAL_50:%.*]] = tensor.cast [[VAL_51:%.*]] : tensor<1x3xf32> to tensor<1x?xf32>

// `tfl.concatenation`.

func.func @concatenate_float(%arg0: tensor<3x2xf32>, %arg1: tensor<1x2xf32>) -> tensor<4x2xf32> {
  %0 = "stablehlo.concatenate"(%arg0, %arg1) {dimension = 0 : i64} : (tensor<3x2xf32>, tensor<1x2xf32>) -> tensor<4x2xf32>
  return %0 : tensor<4x2xf32>
}
// CHECK-LABEL: concatenate_float
// CHECK-NOT: tfl.concatenation
// CHECK: stablehlo.concatenate

// -----

}

func.func @approx_top_k_with_min_k(%arg0: tensor<1x4xf32>) -> (tensor<1x4xf32>, tensor<1x4xi32>) {
  %values, %indices = "tf.ApproxTopK"(%arg0) {aggregate_to_topk = true, is_max_k = false, k = 4 : i64, recall_target = 8.500000e-01 : f32, reduction_dimension = 1 : i64, reduction_input_size_override = -1 : i64} : (tensor<1x4xf32>) -> (tensor<1x4xf32>, tensor<1x4xi32>)
  func.return %values, %indices: tensor<1x4xf32>, tensor<1x4xi32>

    ```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

    }) {is_stateless = false} : (tensor<i32>, tensor<!tf_type.variant<tensor<?x1xf32>>>) -> (tensor<i32>, tensor<!tf_type.variant<tensor<?x1xf32>>>)
    %elem_1 = "tf._SomeOtherOp"() : () -> tensor<8x1xf32>
    %tl_set_item = "tf.TensorListSetItem"(%while#1, %one, %elem_1) : (tensor<!tf_type.variant<tensor<?x1xf32>>>, tensor<i32>, tensor<8x1xf32>) -> tensor<!tf_type.variant<tensor<?x1xf32>>>
    func.return
  }

    "tf.AssignVariableOp"(%arg0, %partitioned_output#0) : (tensor<!tf_type.resource<tensor<3x2xf32>>>, tensor<3x2xf32>) -> ()
    "tf.AssignVariableOp"(%arg1, %partitioned_output#1) : (tensor<!tf_type.resource<tensor<3x2xf32>>>, tensor<3x2xf32>) -> ()
    func.return
  }
  func.func @computation(%arg0: tensor<3x4xf32>) -> tensor<3x4xf32> {
    func.return %arg0: tensor<3x4xf32>
  }
}

// -----