package a

func Conv(v interface{}) string {
	return conv[string](v)
}

func conv[T any](v interface{}) T {
	return v.(T)
}

func Conv2(v interface{}) (string, bool) {
	return conv2[string](v)
}

func conv2[T any](v interface{}) (T, bool) {
	x, ok := v.(T)
	return x, ok
}

func Conv3(v interface{}) string {
	return conv3[string](v)
}

func conv3[T any](v interface{}) T {

package main

import (
	"./a"
	"fmt"
)

func main() {
	s := "foo"
	x := a.Conv(s)
	if x != s {
		panic(fmt.Sprintf("got %s wanted %s", x, s))
	}
	y, ok := a.Conv2(s)
	if !ok {
		panic("conversion failed")
	}
	if y != s {
		panic(fmt.Sprintf("got %s wanted %s", y, s))
	}
	z := a.Conv3(s)
	if z != s {
		panic(fmt.Sprintf("got %s wanted %s", z, s))
	}

    # output shape: [-1, 14, 14, 64]
    conv2 = gen_mnist_ops.new_conv2d(max_pool1, self.weights['f2'],
                                     self.biases['b2'], 1, 1, 1, 1, 'SAME',
                                     'RELU')

    # output shape: [-1, 7, 7, 64]
    max_pool2 = gen_mnist_ops.new_max_pool(conv2, 2, 2, 2, 2, 'SAME')

         explicit_paddings = [],
         padding = "VALID",
         strides = [1, 1, 1, 1]
       } : (tensor<?x64x56x56xf32>, tensor<1x1x64x256xf32>) -> tensor<?x256x56x56xf32>

  // CHECK: %[[CONV2:[0-9]*]] = "tf.Conv2D"(%[[MAX_POOL]], %arg4)
  // CHECK-SAME: data_format = "NHWC"

  %12, %batch_mean_2, %batch_variance_2, %reserved_2_1, %reserved_2_2, %reserved_2_3 =