func.return %0 : tensor<4x128xf32>
  }
  func.func @_func(%arg0: tensor<4x128xf32>) -> tensor<4x128xf32> {
    func.return %arg0 : tensor<4x128xf32>
  }

func.func @testAddV2IdentityBroadcastTensor(%arg0: tensor<4x1xf32>, %arg1: tensor<4x2xf32>) -> (tensor<4x2xf32>, tensor<4x2xf32>, tensor<4x2xf32>, tensor<4x2xf32>) {
  %0 = "tf.Const"() {value = dense<0.0> : tensor<1x2xf32>} : () -> tensor<1x2xf32>

  // Operand and identity shapes are broadcastable. However, we cannot fold
  // because the operand does not match the result shape.
  %1 = "tf.AddV2"(%arg0, %0) : (tensor<4x1xf32>, tensor<1x2xf32>) -> tensor<4x2xf32>

func.func @testPack(%arg0: tensor<4x8xf32>, %arg1: tensor<4x8xf32>) -> tensor<*xf32> {
  %0 = "tf.Pack"(%arg0, %arg1) {axis = 1 : i64} : (tensor<4x8xf32>, tensor<4x8xf32>) -> tensor<*xf32>
  func.return %0 : tensor<*xf32>
}


// -----

func.func @testPack(%arg0: tensor<4x8xf32>, %arg1: tensor<4x2xf32>) -> tensor<*xf32> {

    // CHECK: (tensor<!tf_type.string>, tensor<1x!tf_type.string>) -> tensor<128xf32>
    %3 = "tf.Restore"(%arg0, %cst_0) {device = ""} : (tensor<!tf_type.string>, tensor<1x!tf_type.string>) -> tensor<*xf32>
    "tf.AssignVariableOp"(%2, %3) : (tensor<!tf_type.resource<tensor<128xf32>>>, tensor<*xf32>) -> ()

}

// -----

// 'input_to_output_weights' input for lstm op has unmatched rank with `input`.