// CHECK: %[[conv:.*]] = "tfl.conv_2d"(%arg0, %[[w3]], %[[b]])
// CHECK: %[[dconv:.*]] = "tfl.depthwise_conv_2d"(%arg0, %[[w2]], %[[b]])
// CHECK: %[[emb:.*]] = "tfl.gather"(%[[dq_w1]], %arg1)
// CHECK: %[[bmm:.*]] = "tfl.batch_matmul"(%[[conv]], %[[dconv]]) <{adj_x = false, adj_y = true
// CHECK-NOT: , asymmetric_quantize_inputs = true
// CHECK-SAME: }

// CHECK-DAG: %[[dq_w1:.*]] = "tfl.dequantize"(%[[q_w1]])
// CHECK-DAG: %[[dq_w2:.*]] = "tfl.dequantize"(%[[q_w2]])
// CHECK: %[[conv:.*]] = "tfl.conv_2d"(%arg0, %[[dq_w2]], %[[b]])
// CHECK: %[[dconv:.*]] = "tfl.depthwise_conv_2d"(%arg0, %[[dq_w1]], %[[b]])
// CHECK: %[[bmm:.*]] = "tfl.batch_matmul"(%[[conv]], %[[dconv]]) <{adj_x = false, adj_y = true
// CHECK-NOT: , asymmetric_quantize_inputs = true
// CHECK-SAME: }
// CHECK: return %[[bmm:.*]]

// as long as they register proper cconv function for it.
type opSuffixSet struct {
	arch  string
	cconv func(suffix uint8) string
}

var opSuffixSpace []opSuffixSet

// RegisterOpSuffix assigns cconv function for formatting opcode suffixes
// when compiling for GOARCH=arch.
//
// cconv is never called with 0 argument.
func RegisterOpSuffix(arch string, cconv func(uint8) string) {

// CHECK: %[[CONV:.+]] = stablehlo.convolution(%[[TRANSPOSE_0]], %[[CONST]]) dim_numbers = [b, 0, 1, f]x[0, 1, i, o]->[b, 0, 1, f], window = {pad = {{\[\[}}1, 1], [1, 1]]} {batch_group_count = 1 : i64, feature_group_count = 1 : i64} : (tensor<1x4x4x8xf32>, tensor<3x3x8x8xf32>) -> tensor<1x4x4x8xf32>
// CHECK: %[[TRANSPOSE_1:.+]] = stablehlo.transpose %[[CONV]], dims = [0, 3, 1, 2] : (tensor<1x4x4x8xf32>) -> tensor<1x8x4x4xf32>

	// 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-DAG: %[[CONV:.*]] = stablehlo.convolution(%[[ARG]], %[[CONST_1]]) {{.*}} : (tensor<1x3x4x3xf32>, tensor<2x3x3x2xf32>) -> tensor<1x3x2x2xf32>
// CHECK-DAG: %[[BROADCAST:.*]] = stablehlo.broadcast_in_dim %[[CONST_0]], dims = [3] : (tensor<2xf32>) -> tensor<1x3x2x2xf32>
// CHECK-DAG: %[[ADD:.*]] = stablehlo.add %[[CONV]], %[[BROADCAST]] : tensor<1x3x2x2xf32>

  // attribute which is shared.
  bool AreFuseCompatible(Conv2DOp conv, BiasAddOp bias_add,
                         PatternRewriter &rewriter) const override {
    // Verify that the data formats match and are valid for fusion.
    if (conv.getDataFormat() != bias_add.getDataFormat()) {
      (void)rewriter.notifyMatchFailure(conv, [&](Diagnostic &diag) {
        diag << "data format does not match Conv2D data format ("

  %conv = "tf.Conv2D"(%dq_input, %dq_weight) {attr_map = "0:strides,1:use_cudnn_on_gpu,2:padding,3:explicit_paddings,4:dilations", data_format = "NHWC", device = "", dilations = [1, 1, 1, 1], explicit_paddings = [], padding = "VALID", strides = [1, 1, 2, 1], use_cudnn_on_gpu = true} : (tensor<1x3x4x3xf32>, tensor<2x3x3x2xf32>) -> tensor<*xf32>

  %b = arith.constant dense<0.0> : tensor<3xf32>
  %conv = "tfl.conv_2d"(%arg0, %w, %b) {dilation_h_factor = 1 : i32, dilation_w_factor = 1 : i32, fused_activation_function = "RELU", padding = "SAME", stride_h = 1 : i32, stride_w = 1 : i32} : (tensor<1x5x5x3xf32>, tensor<3x3x3x3xf32>, tensor<3xf32>) -> tensor<1x5x5x3xf32>
  func.return %conv : tensor<1x5x5x3xf32>

// CHECK-NEXT: %[[cst2:.*]] = "tfl.pseudo_qconst"() <{qtype = tensor<32x!quant.uniform<i32:f32, 1.7052092479439231E-4>>, value = dense<0> : tensor<32xi32>}> : () -> tensor<32x!quant.uniform<i32:f32, 1.7052092479439231E-4>>