// 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) {

    key: "narrow_range"
    value {
      b: true
    }
  }
  attr {
    key: "num_bits"
    value {
      i: 8
    }
  }
}
node {
  name: "BoxPredictor_4/ClassPredictor/Conv2D"
  op: "Conv2D"
  input: "input"
  input: "BoxPredictor_4/ClassPredictor/weights_quant/FakeQuantWithMinMaxVarsPerChannel"
  attr {
    key: "T"
    value {
      type: DT_FLOAT
    }
  }
  attr {

  %b = arith.constant dense<-1.23697901> : tensor<64xf32>
  %conv = "tfl.conv_2d"(%0, %w, %b) {dilation_h_factor = 1 : i32, dilation_w_factor = 1 : i32, fused_activation_function = "NONE", padding = "SAME", stride_h = 2 : i32, stride_w = 2 : i32} : (tensor<1x224x224x3xf32>, tensor<64x3x3x3xf32>, tensor<64xf32>) -> tensor<1x112x112x64xf32>

        conf1.outgoingEdges >> [dep(selector2, 2, 3)]

        def conf2 = node(2, "org", "dep1", "1.0")
        def selector3 = selector(12, "org", "dep3", "1.0")
        conf2.outgoingEdges >> [dep(selector3, 2, 4)]

        builder.start(root)

        builder.visitNode(root)
        builder.visitNode(conf1)
        builder.visitNode(conf2)
        builder.visitNode(node(3, "org", "dep2", "1.0"))

// 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>

    save_options = None
    if has_func_alias:
      save_options = tensorflow.saved_model.SaveOptions(
          function_aliases={FUNC_ALIAS: model.conv2d}
      )
    saved_model_save.save(
        model,
        saved_model_path,
        signatures=model.conv2d.get_concrete_function(
            tensor_spec.TensorSpec(
                shape=input_shape, dtype=dtypes.float32, name='input_tensor'
            )

    key: "narrow_range"
    value {
      b: true
    }
  }
  attr {
    key: "num_bits"
    value {
      i: 8
    }
  }
}
node {
  name: "BoxPredictor_4/ClassPredictor/Conv2D"
  op: "Conv2D"
  input: "input"
  input: "BoxPredictor_4/ClassPredictor/weights_quant/FakeQuantWithMinMaxVarsPerChannel"
  attr {
    key: "T"
    value {
      type: DT_FLOAT
    }
  }
  attr {

    key: "narrow_range"
    value {
      b: true
    }
  }
  attr {
    key: "num_bits"
    value {
      i: 4
    }
  }
}
node {
  name: "BoxPredictor_4/ClassPredictor/Conv2D"
  op: "Conv2D"
  input: "input"
  input: "BoxPredictor_4/ClassPredictor/weights_quant/FakeQuantWithMinMaxVarsPerChannel"
  attr {
    key: "T"
    value {
      type: DT_FLOAT
    }
  }
  attr {

    func.return %add : tensor<*xf32>
  }

  func.func @composite_conv2d_with_relu6_fn(%arg0: tensor<*xf32>, %arg1: tensor<*xf32>) -> tensor<*xf32> attributes {tf_quant.composite_function} {

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