// Use of this source code is governed by a BSD-style
// license that can be found in the LICENSE file.

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))
	}

// CHECK: %[[CONV:.+]] = stablehlo.convolution(%[[ARG1]], %[[ARG2]])
// CHECK-SAME: (tensor<1x3x4x3xf32>, tensor<2x3x3x2x!quant.uniform<i8:f32, 6.000000e-03:-128>>) -> tensor<1x3x4x2xf32>

Zichuan Wei <******@****.***> 1677539988 -0800

// CHECK: %[[cast:.*]] = "tf.Cast"(%[[cst]]) <{Truncate = false}> : (tensor<2x3x3x2xbf16>) -> tensor<2x3x3x2xf32>
// CHECK: %[[conv:.*]] = "tf.Conv2D"(%[[ARG0]], %[[cast]])
// CHECK: %[[identity:.*]] = "tf.IdentityN"(%[[conv]]) {device = ""} : (tensor<1x3x2x2xf32>) -> tensor<1x3x2x2xf32>
// CHECK: return %[[identity]] : tensor<1x3x2x2xf32>

// -----

// Tests that `tf.BatchFunction` is inlined.

	"HS",
	"LO",
	"MI",
	"PL",
	"VS",
	"VC",
	"HI",
	"LS",
	"GE",
	"LT",
	"GT",
	"LE",
	"AL",
	"NV",
}

func init() {
	obj.RegisterRegister(obj.RBaseARM64, REG_SPECIAL+1024, rconv)
	obj.RegisterOpcode(obj.ABaseARM64, Anames)
	obj.RegisterRegisterList(obj.RegListARM64Lo, obj.RegListARM64Hi, rlconv)
	obj.RegisterOpSuffix("arm64", obj.CConvARM)

num_channels = 1

# Training parameters.
learning_rate = 0.001
display_step = 10
batch_size = 32

# Network parameters.
n_hidden_1 = 32  # 1st conv layer number of neurons.
n_hidden_2 = 64  # 2nd conv layer number of neurons.
n_hidden_3 = 64  # 1st fully connected layer of neurons.
flatten_size = num_features // 16 * n_hidden_2

seed = 66478


class FloatModel(tf.Module):

                             %arg2: tensor<256xf32>,          // batch_norm args
                             %arg3: tensor<7x7x3x64xf32>,    // conv filter #0
                             %arg4: tensor<1x1x64x256xf32>   // conv filter #1
                            ) -> tensor<?x256xf32> {

  // This is a simplified ResNet layer that gets input in NHWC format, converts

// CHECK: %[[CONV:.*]] = stablehlo.convolution(%[[QUANT_ARG]], %[[FILTER]]) {{.*}} : (tensor<1x3x3x4x!quant.uniform<i8:f32, {{.*}}>>, tensor<3x3x4x4x!quant.uniform<i8:f32:3, {{.*}}>>) -> tensor<1x3x3x4x!quant.uniform<i8:f32, {{.*}}>>
// CHECK: %[[DEQUANT:.*]] = stablehlo.uniform_dequantize %[[CONV]] : (tensor<1x3x3x4x!quant.uniform<i8:f32, {{.*}}>>) -> tensor<1x3x3x4xf32>

				reportf(arg.Pos(), "possibly passing Go type with embedded pointer to C")
				break
			}

			// Check for passing the address of a bad type.
			if conv, ok := arg.(*ast.CallExpr); ok && len(conv.Args) == 1 &&
				isUnsafePointer(info, conv.Fun) {
				arg = conv.Args[0]
			}
			if u, ok := arg.(*ast.UnaryExpr); ok && u.Op == token.AND {
				if !typeOKForCgoCall(cgoBaseType(info, u.X), make(map[types.Type]bool)) {

		ir.ORECOVERFP,
		ir.OSTR2BYTES,
		ir.OSTR2BYTESTMP,
		ir.OSTR2RUNES:

		if isRuneCount(n) {
			// len([]rune(s)) is rewritten to runtime.countrunes(s) later.
			conv := n.(*ir.UnaryExpr).X.(*ir.ConvExpr)
			conv.X = o.expr(conv.X, nil)
		} else {
			o.call(n)
		}

		if lhs == nil || lhs.Op() != ir.ONAME || base.Flag.Cfg.Instrumenting {
			return o.copyExpr(n)
		}
		return n