// 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.

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

	// if uint(newLen) <= uint(oldCap)
	nif := ir.NewIfStmt(base.Pos, nil, nil, nil)
	nuint := typecheck.Conv(newLen, types.Types[types.TUINT])
	scapuint := typecheck.Conv(oldCap, types.Types[types.TUINT])
	nif.Cond = ir.NewBinaryExpr(base.Pos, ir.OLE, nuint, scapuint)
	nif.Likely = true

	// then { s = s[:newLen] }

// 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)) {

func countrunes(string) int

// Convert non-interface type to the data word of a (empty or nonempty) interface.
func convT(typ *byte, elem *any) unsafe.Pointer

// Same as convT, for types with no pointers in them.
func convTnoptr(typ *byte, elem *any) unsafe.Pointer

// Specialized versions of convT for specific types.
// These functions take concrete types in the runtime. But they may

// point constant.
def : Pat<(TFL_DequantizeOp
             (TFL_QuantizeOp (Arith_ConstantOp F32ElementsAttr:$cst), $qt)),
          (TFL_ConstOp $cst)>;

// Transpose conv supports hybrid computation with quantized weights.
def FoldQuantWeightsIntoTposeConv : Pat<
  (TFL_TransposeConvOp
    $output_shape,
    (TFL_DequantizeOp $quant_weights),
    $quant_input,

                      weight_new_shape, &rewriter);

  // Replace the fc with conv.
  // The output would be [1, 1, width, output].
  auto conv_output_type = RankedTensorType::get({1, 1, width, output_size},
                                                output_type.getElementType());
  auto conv = rewriter.create<TFL::Conv2DOp>(
      fc_op.getLoc(), conv_output_type, reshaped_input, reshaped_weight,

// functions fail on a nil input.

// convT converts a value of type t, which is pointed to by v, to a pointer that can
// be used as the second word of an interface value.
func convT(t *_type, v unsafe.Pointer) unsafe.Pointer {
	if raceenabled {
		raceReadObjectPC(t, v, getcallerpc(), abi.FuncPCABIInternal(convT))
	}
	if msanenabled {
		msanread(v, t.Size_)
	}
	if asanenabled {

      mlir::mhlo::createLegalizeDotToDotGeneralPass());
  // Unfuse mhlo BatchNorm to primitive ops.
  pm.addNestedPass<mlir::func::FuncOp>(mlir::odml::createUnfuseBatchNormPass());
  // Fuse Conv + Mul to Conv.
  pm.addNestedPass<mlir::func::FuncOp>(mlir::odml::createFuseConvolutionPass());
  // Fold broadcast_in_dim + Mul.
  pm.addNestedPass<mlir::func::FuncOp>(mlir::odml::createFoldBroadcastPass());