%1 = "tfl.reshape"(%0, %shape) : (tensor<40x40xf32>, tensor<4xi32>) -> tensor<1x40x4x10xf32>
  %2 = "tfl.add"(%1, %cst2) {fused_activation_function = "NONE"} : (tensor<1x40x4x10xf32>, tensor<1x1x4x10xf32>) -> tensor<1x40x4x10xf32>

  func.return %2 : tensor<1x40x4x10xf32>

  // CHECK-DAG: %[[cst:.*]] = arith.constant dense<2.000000e+00> : tensor<40xf32>

// CHECK:           %[[VAL_2:.*]] = "tf.Conv2D"(%[[VAL_0]], %[[VAL_1]]) <{data_format = "NHWC", dilations = [1, 1, 1, 1], explicit_paddings = [], padding = "VALID", strides = [1, 1, 1, 1], use_cudnn_on_gpu = true}> : (tensor<1x6x6x207xf32>, tensor<3x3x207x16xf32>) -> tensor<1x4x4x16xf32>
// CHECK:           return %[[VAL_2]] : tensor<1x4x4x16xf32>
// CHECK:         }

// -----

// CHECK-LABEL: conv_valid_padding
func.func @conv_valid_padding(%arg0: tensor<1x4x5x1xf32>, %arg1: tensor<3x3x1x1xf32>) -> tensor<1x2x3x1xf32> {
  // CHECK: mhlo.convolution(%arg0, %arg1)

  %0 = "tf.Conv2D"(%arg0, %arg1) {data_format = "NHWC", dilations = [1, 1, 1, 1], padding = "VALID", strides = [1, 1, 1, 1]} : (tensor<1x4x5x1xf32>, tensor<3x3x1x1xf32>) -> tensor<1x2x3x1xf32>
  func.return %0 : tensor<1x2x3x1xf32>
}