diff --git "a/cfa/mvtec/leather/run/weights/openvino/model.xml" "b/cfa/mvtec/leather/run/weights/openvino/model.xml"
new file mode 100755--- /dev/null
+++ "b/cfa/mvtec/leather/run/weights/openvino/model.xml"
@@ -0,0 +1,7304 @@
+<?xml version="1.0"?>
+<net name="torch_jit" version="11">
+	<layers>
+		<layer id="0" name="input" type="Parameter" version="opset1">
+			<data shape="1,3,224,224" element_type="f32" />
+			<output>
+				<port id="0" precision="FP32" names="input">
+					<dim>1</dim>
+					<dim>3</dim>
+					<dim>224</dim>
+					<dim>224</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="1" name="onnx::Conv_701" type="Const" version="opset1">
+			<data element_type="f32" shape="64, 3, 7, 7" offset="0" size="37632" />
+			<output>
+				<port id="0" precision="FP32" names="onnx::Conv_701">
+					<dim>64</dim>
+					<dim>3</dim>
+					<dim>7</dim>
+					<dim>7</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="2" name="Conv_23/WithoutBiases" type="Convolution" version="opset1">
+			<data strides="2, 2" dilations="1, 1" pads_begin="3, 3" pads_end="3, 3" auto_pad="explicit" />
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>3</dim>
+					<dim>224</dim>
+					<dim>224</dim>
+				</port>
+				<port id="1" precision="FP32">
+					<dim>64</dim>
+					<dim>3</dim>
+					<dim>7</dim>
+					<dim>7</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="FP32">
+					<dim>1</dim>
+					<dim>64</dim>
+					<dim>112</dim>
+					<dim>112</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="3" name="Reshape_120" type="Const" version="opset1">
+			<data element_type="f32" shape="1, 64, 1, 1" offset="37632" size="256" />
+			<output>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>64</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="4" name="Conv_23" type="Add" version="opset1">
+			<data auto_broadcast="numpy" />
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>64</dim>
+					<dim>112</dim>
+					<dim>112</dim>
+				</port>
+				<port id="1" precision="FP32">
+					<dim>1</dim>
+					<dim>64</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="FP32" names="input.4">
+					<dim>1</dim>
+					<dim>64</dim>
+					<dim>112</dim>
+					<dim>112</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="5" name="Relu_24" type="ReLU" version="opset1">
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>64</dim>
+					<dim>112</dim>
+					<dim>112</dim>
+				</port>
+			</input>
+			<output>
+				<port id="1" precision="FP32" names="onnx::MaxPool_266">
+					<dim>1</dim>
+					<dim>64</dim>
+					<dim>112</dim>
+					<dim>112</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="6" name="MaxPool_25" type="MaxPool" version="opset8">
+			<data strides="2, 2" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" kernel="3, 3" rounding_type="floor" auto_pad="explicit" index_element_type="i64" axis="0" />
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>64</dim>
+					<dim>112</dim>
+					<dim>112</dim>
+				</port>
+			</input>
+			<output>
+				<port id="1" precision="FP32" names="input.8">
+					<dim>1</dim>
+					<dim>64</dim>
+					<dim>56</dim>
+					<dim>56</dim>
+				</port>
+				<port id="2" precision="I64">
+					<dim>1</dim>
+					<dim>64</dim>
+					<dim>56</dim>
+					<dim>56</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="7" name="onnx::Conv_704" type="Const" version="opset1">
+			<data element_type="f32" shape="128, 64, 1, 1" offset="37888" size="32768" />
+			<output>
+				<port id="0" precision="FP32" names="onnx::Conv_704">
+					<dim>128</dim>
+					<dim>64</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="8" name="Conv_26/WithoutBiases" type="Convolution" version="opset1">
+			<data strides="1, 1" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" auto_pad="explicit" />
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>64</dim>
+					<dim>56</dim>
+					<dim>56</dim>
+				</port>
+				<port id="1" precision="FP32">
+					<dim>128</dim>
+					<dim>64</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="FP32">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>56</dim>
+					<dim>56</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="9" name="Reshape_170" type="Const" version="opset1">
+			<data element_type="f32" shape="1, 128, 1, 1" offset="70656" size="512" />
+			<output>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="10" name="Conv_26" type="Add" version="opset1">
+			<data auto_broadcast="numpy" />
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>56</dim>
+					<dim>56</dim>
+				</port>
+				<port id="1" precision="FP32">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="FP32" names="input.16">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>56</dim>
+					<dim>56</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="11" name="Relu_27" type="ReLU" version="opset1">
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>56</dim>
+					<dim>56</dim>
+				</port>
+			</input>
+			<output>
+				<port id="1" precision="FP32" names="onnx::Conv_270">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>56</dim>
+					<dim>56</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="12" name="onnx::Conv_707" type="Const" version="opset1">
+			<data element_type="f32" shape="128, 128, 3, 3" offset="71168" size="589824" />
+			<output>
+				<port id="0" precision="FP32" names="onnx::Conv_707">
+					<dim>128</dim>
+					<dim>128</dim>
+					<dim>3</dim>
+					<dim>3</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="13" name="Conv_28/WithoutBiases" type="Convolution" version="opset1">
+			<data strides="1, 1" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" auto_pad="explicit" />
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>56</dim>
+					<dim>56</dim>
+				</port>
+				<port id="1" precision="FP32">
+					<dim>128</dim>
+					<dim>128</dim>
+					<dim>3</dim>
+					<dim>3</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="FP32">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>56</dim>
+					<dim>56</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="14" name="Reshape_219" type="Const" version="opset1">
+			<data element_type="f32" shape="1, 128, 1, 1" offset="660992" size="512" />
+			<output>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="15" name="Conv_28" type="Add" version="opset1">
+			<data auto_broadcast="numpy" />
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>56</dim>
+					<dim>56</dim>
+				</port>
+				<port id="1" precision="FP32">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="FP32" names="input.24">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>56</dim>
+					<dim>56</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="16" name="Relu_29" type="ReLU" version="opset1">
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>56</dim>
+					<dim>56</dim>
+				</port>
+			</input>
+			<output>
+				<port id="1" precision="FP32" names="onnx::Conv_273">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>56</dim>
+					<dim>56</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="17" name="onnx::Conv_710" type="Const" version="opset1">
+			<data element_type="f32" shape="256, 128, 1, 1" offset="661504" size="131072" />
+			<output>
+				<port id="0" precision="FP32" names="onnx::Conv_710">
+					<dim>256</dim>
+					<dim>128</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="18" name="Conv_30/WithoutBiases" type="Convolution" version="opset1">
+			<data strides="1, 1" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" auto_pad="explicit" />
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>56</dim>
+					<dim>56</dim>
+				</port>
+				<port id="1" precision="FP32">
+					<dim>256</dim>
+					<dim>128</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="FP32">
+					<dim>1</dim>
+					<dim>256</dim>
+					<dim>56</dim>
+					<dim>56</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="19" name="Reshape_268" type="Const" version="opset1">
+			<data element_type="f32" shape="1, 256, 1, 1" offset="792576" size="1024" />
+			<output>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>256</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="20" name="Conv_30" type="Add" version="opset1">
+			<data auto_broadcast="numpy" />
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>256</dim>
+					<dim>56</dim>
+					<dim>56</dim>
+				</port>
+				<port id="1" precision="FP32">
+					<dim>1</dim>
+					<dim>256</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="FP32" names="onnx::Add_709">
+					<dim>1</dim>
+					<dim>256</dim>
+					<dim>56</dim>
+					<dim>56</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="21" name="onnx::Conv_713" type="Const" version="opset1">
+			<data element_type="f32" shape="256, 64, 1, 1" offset="793600" size="65536" />
+			<output>
+				<port id="0" precision="FP32" names="onnx::Conv_713">
+					<dim>256</dim>
+					<dim>64</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="22" name="Conv_31/WithoutBiases" type="Convolution" version="opset1">
+			<data strides="1, 1" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" auto_pad="explicit" />
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>64</dim>
+					<dim>56</dim>
+					<dim>56</dim>
+				</port>
+				<port id="1" precision="FP32">
+					<dim>256</dim>
+					<dim>64</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="FP32">
+					<dim>1</dim>
+					<dim>256</dim>
+					<dim>56</dim>
+					<dim>56</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="23" name="Reshape_316" type="Const" version="opset1">
+			<data element_type="f32" shape="1, 256, 1, 1" offset="859136" size="1024" />
+			<output>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>256</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="24" name="Conv_31" type="Add" version="opset1">
+			<data auto_broadcast="numpy" />
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>256</dim>
+					<dim>56</dim>
+					<dim>56</dim>
+				</port>
+				<port id="1" precision="FP32">
+					<dim>1</dim>
+					<dim>256</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="FP32" names="onnx::Add_712">
+					<dim>1</dim>
+					<dim>256</dim>
+					<dim>56</dim>
+					<dim>56</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="25" name="Add_32" type="Add" version="opset1">
+			<data auto_broadcast="numpy" />
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>256</dim>
+					<dim>56</dim>
+					<dim>56</dim>
+				</port>
+				<port id="1" precision="FP32">
+					<dim>1</dim>
+					<dim>256</dim>
+					<dim>56</dim>
+					<dim>56</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="FP32" names="input.36">
+					<dim>1</dim>
+					<dim>256</dim>
+					<dim>56</dim>
+					<dim>56</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="26" name="Relu_33" type="ReLU" version="opset1">
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>256</dim>
+					<dim>56</dim>
+					<dim>56</dim>
+				</port>
+			</input>
+			<output>
+				<port id="1" precision="FP32" names="onnx::Conv_279">
+					<dim>1</dim>
+					<dim>256</dim>
+					<dim>56</dim>
+					<dim>56</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="27" name="onnx::Conv_716" type="Const" version="opset1">
+			<data element_type="f32" shape="128, 256, 1, 1" offset="860160" size="131072" />
+			<output>
+				<port id="0" precision="FP32" names="onnx::Conv_716">
+					<dim>128</dim>
+					<dim>256</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="28" name="Conv_34/WithoutBiases" type="Convolution" version="opset1">
+			<data strides="1, 1" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" auto_pad="explicit" />
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>256</dim>
+					<dim>56</dim>
+					<dim>56</dim>
+				</port>
+				<port id="1" precision="FP32">
+					<dim>128</dim>
+					<dim>256</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="FP32">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>56</dim>
+					<dim>56</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="29" name="Reshape_366" type="Const" version="opset1">
+			<data element_type="f32" shape="1, 128, 1, 1" offset="991232" size="512" />
+			<output>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="30" name="Conv_34" type="Add" version="opset1">
+			<data auto_broadcast="numpy" />
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>56</dim>
+					<dim>56</dim>
+				</port>
+				<port id="1" precision="FP32">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="FP32" names="input.44">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>56</dim>
+					<dim>56</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="31" name="Relu_35" type="ReLU" version="opset1">
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>56</dim>
+					<dim>56</dim>
+				</port>
+			</input>
+			<output>
+				<port id="1" precision="FP32" names="onnx::Conv_282">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>56</dim>
+					<dim>56</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="32" name="onnx::Conv_719" type="Const" version="opset1">
+			<data element_type="f32" shape="128, 128, 3, 3" offset="991744" size="589824" />
+			<output>
+				<port id="0" precision="FP32" names="onnx::Conv_719">
+					<dim>128</dim>
+					<dim>128</dim>
+					<dim>3</dim>
+					<dim>3</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="33" name="Conv_36/WithoutBiases" type="Convolution" version="opset1">
+			<data strides="1, 1" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" auto_pad="explicit" />
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>56</dim>
+					<dim>56</dim>
+				</port>
+				<port id="1" precision="FP32">
+					<dim>128</dim>
+					<dim>128</dim>
+					<dim>3</dim>
+					<dim>3</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="FP32">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>56</dim>
+					<dim>56</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="34" name="Reshape_415" type="Const" version="opset1">
+			<data element_type="f32" shape="1, 128, 1, 1" offset="1581568" size="512" />
+			<output>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="35" name="Conv_36" type="Add" version="opset1">
+			<data auto_broadcast="numpy" />
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>56</dim>
+					<dim>56</dim>
+				</port>
+				<port id="1" precision="FP32">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="FP32" names="input.52">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>56</dim>
+					<dim>56</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="36" name="Relu_37" type="ReLU" version="opset1">
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>56</dim>
+					<dim>56</dim>
+				</port>
+			</input>
+			<output>
+				<port id="1" precision="FP32" names="onnx::Conv_285">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>56</dim>
+					<dim>56</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="37" name="onnx::Conv_722" type="Const" version="opset1">
+			<data element_type="f32" shape="256, 128, 1, 1" offset="1582080" size="131072" />
+			<output>
+				<port id="0" precision="FP32" names="onnx::Conv_722">
+					<dim>256</dim>
+					<dim>128</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="38" name="Conv_38/WithoutBiases" type="Convolution" version="opset1">
+			<data strides="1, 1" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" auto_pad="explicit" />
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>56</dim>
+					<dim>56</dim>
+				</port>
+				<port id="1" precision="FP32">
+					<dim>256</dim>
+					<dim>128</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="FP32">
+					<dim>1</dim>
+					<dim>256</dim>
+					<dim>56</dim>
+					<dim>56</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="39" name="Reshape_464" type="Const" version="opset1">
+			<data element_type="f32" shape="1, 256, 1, 1" offset="1713152" size="1024" />
+			<output>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>256</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="40" name="Conv_38" type="Add" version="opset1">
+			<data auto_broadcast="numpy" />
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>256</dim>
+					<dim>56</dim>
+					<dim>56</dim>
+				</port>
+				<port id="1" precision="FP32">
+					<dim>1</dim>
+					<dim>256</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="FP32" names="onnx::Add_721">
+					<dim>1</dim>
+					<dim>256</dim>
+					<dim>56</dim>
+					<dim>56</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="41" name="Add_39" type="Add" version="opset1">
+			<data auto_broadcast="numpy" />
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>256</dim>
+					<dim>56</dim>
+					<dim>56</dim>
+				</port>
+				<port id="1" precision="FP32">
+					<dim>1</dim>
+					<dim>256</dim>
+					<dim>56</dim>
+					<dim>56</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="FP32" names="input.60">
+					<dim>1</dim>
+					<dim>256</dim>
+					<dim>56</dim>
+					<dim>56</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="42" name="Relu_40" type="ReLU" version="opset1">
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>256</dim>
+					<dim>56</dim>
+					<dim>56</dim>
+				</port>
+			</input>
+			<output>
+				<port id="1" precision="FP32" names="onnx::Conv_289">
+					<dim>1</dim>
+					<dim>256</dim>
+					<dim>56</dim>
+					<dim>56</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="43" name="onnx::Conv_725" type="Const" version="opset1">
+			<data element_type="f32" shape="128, 256, 1, 1" offset="1714176" size="131072" />
+			<output>
+				<port id="0" precision="FP32" names="onnx::Conv_725">
+					<dim>128</dim>
+					<dim>256</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="44" name="Conv_41/WithoutBiases" type="Convolution" version="opset1">
+			<data strides="1, 1" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" auto_pad="explicit" />
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>256</dim>
+					<dim>56</dim>
+					<dim>56</dim>
+				</port>
+				<port id="1" precision="FP32">
+					<dim>128</dim>
+					<dim>256</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="FP32">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>56</dim>
+					<dim>56</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="45" name="Reshape_514" type="Const" version="opset1">
+			<data element_type="f32" shape="1, 128, 1, 1" offset="1845248" size="512" />
+			<output>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="46" name="Conv_41" type="Add" version="opset1">
+			<data auto_broadcast="numpy" />
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>56</dim>
+					<dim>56</dim>
+				</port>
+				<port id="1" precision="FP32">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="FP32" names="input.68">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>56</dim>
+					<dim>56</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="47" name="Relu_42" type="ReLU" version="opset1">
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>56</dim>
+					<dim>56</dim>
+				</port>
+			</input>
+			<output>
+				<port id="1" precision="FP32" names="onnx::Conv_292">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>56</dim>
+					<dim>56</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="48" name="onnx::Conv_728" type="Const" version="opset1">
+			<data element_type="f32" shape="128, 128, 3, 3" offset="1845760" size="589824" />
+			<output>
+				<port id="0" precision="FP32" names="onnx::Conv_728">
+					<dim>128</dim>
+					<dim>128</dim>
+					<dim>3</dim>
+					<dim>3</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="49" name="Conv_43/WithoutBiases" type="Convolution" version="opset1">
+			<data strides="1, 1" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" auto_pad="explicit" />
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>56</dim>
+					<dim>56</dim>
+				</port>
+				<port id="1" precision="FP32">
+					<dim>128</dim>
+					<dim>128</dim>
+					<dim>3</dim>
+					<dim>3</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="FP32">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>56</dim>
+					<dim>56</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="50" name="Reshape_563" type="Const" version="opset1">
+			<data element_type="f32" shape="1, 128, 1, 1" offset="2435584" size="512" />
+			<output>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="51" name="Conv_43" type="Add" version="opset1">
+			<data auto_broadcast="numpy" />
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>56</dim>
+					<dim>56</dim>
+				</port>
+				<port id="1" precision="FP32">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="FP32" names="input.76">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>56</dim>
+					<dim>56</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="52" name="Relu_44" type="ReLU" version="opset1">
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>56</dim>
+					<dim>56</dim>
+				</port>
+			</input>
+			<output>
+				<port id="1" precision="FP32" names="onnx::Conv_295">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>56</dim>
+					<dim>56</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="53" name="onnx::Conv_731" type="Const" version="opset1">
+			<data element_type="f32" shape="256, 128, 1, 1" offset="2436096" size="131072" />
+			<output>
+				<port id="0" precision="FP32" names="onnx::Conv_731">
+					<dim>256</dim>
+					<dim>128</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="54" name="Conv_45/WithoutBiases" type="Convolution" version="opset1">
+			<data strides="1, 1" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" auto_pad="explicit" />
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>56</dim>
+					<dim>56</dim>
+				</port>
+				<port id="1" precision="FP32">
+					<dim>256</dim>
+					<dim>128</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="FP32">
+					<dim>1</dim>
+					<dim>256</dim>
+					<dim>56</dim>
+					<dim>56</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="55" name="Reshape_612" type="Const" version="opset1">
+			<data element_type="f32" shape="1, 256, 1, 1" offset="2567168" size="1024" />
+			<output>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>256</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="56" name="Conv_45" type="Add" version="opset1">
+			<data auto_broadcast="numpy" />
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>256</dim>
+					<dim>56</dim>
+					<dim>56</dim>
+				</port>
+				<port id="1" precision="FP32">
+					<dim>1</dim>
+					<dim>256</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="FP32" names="onnx::Add_730">
+					<dim>1</dim>
+					<dim>256</dim>
+					<dim>56</dim>
+					<dim>56</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="57" name="Add_46" type="Add" version="opset1">
+			<data auto_broadcast="numpy" />
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>256</dim>
+					<dim>56</dim>
+					<dim>56</dim>
+				</port>
+				<port id="1" precision="FP32">
+					<dim>1</dim>
+					<dim>256</dim>
+					<dim>56</dim>
+					<dim>56</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="FP32" names="input.84">
+					<dim>1</dim>
+					<dim>256</dim>
+					<dim>56</dim>
+					<dim>56</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="58" name="Relu_47" type="ReLU" version="opset1">
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>256</dim>
+					<dim>56</dim>
+					<dim>56</dim>
+				</port>
+			</input>
+			<output>
+				<port id="1" precision="FP32" names="onnx::Conv_299">
+					<dim>1</dim>
+					<dim>256</dim>
+					<dim>56</dim>
+					<dim>56</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="59" name="AveragePool_122" type="AvgPool" version="opset1">
+			<data kernel="3, 3" strides="1, 1" pads_begin="1, 1" pads_end="1, 1" exclude-pad="false" auto_pad="explicit" rounding_type="floor" />
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>256</dim>
+					<dim>56</dim>
+					<dim>56</dim>
+				</port>
+			</input>
+			<output>
+				<port id="1" precision="FP32" names="onnx::Concat_406">
+					<dim>1</dim>
+					<dim>256</dim>
+					<dim>56</dim>
+					<dim>56</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="60" name="onnx::Conv_734" type="Const" version="opset1">
+			<data element_type="f32" shape="256, 256, 1, 1" offset="2568192" size="262144" />
+			<output>
+				<port id="0" precision="FP32" names="onnx::Conv_734">
+					<dim>256</dim>
+					<dim>256</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="61" name="Conv_48/WithoutBiases" type="Convolution" version="opset1">
+			<data strides="1, 1" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" auto_pad="explicit" />
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>256</dim>
+					<dim>56</dim>
+					<dim>56</dim>
+				</port>
+				<port id="1" precision="FP32">
+					<dim>256</dim>
+					<dim>256</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="FP32">
+					<dim>1</dim>
+					<dim>256</dim>
+					<dim>56</dim>
+					<dim>56</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="62" name="Reshape_662" type="Const" version="opset1">
+			<data element_type="f32" shape="1, 256, 1, 1" offset="2830336" size="1024" />
+			<output>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>256</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="63" name="Conv_48" type="Add" version="opset1">
+			<data auto_broadcast="numpy" />
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>256</dim>
+					<dim>56</dim>
+					<dim>56</dim>
+				</port>
+				<port id="1" precision="FP32">
+					<dim>1</dim>
+					<dim>256</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="FP32" names="input.92">
+					<dim>1</dim>
+					<dim>256</dim>
+					<dim>56</dim>
+					<dim>56</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="64" name="Relu_49" type="ReLU" version="opset1">
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>256</dim>
+					<dim>56</dim>
+					<dim>56</dim>
+				</port>
+			</input>
+			<output>
+				<port id="1" precision="FP32" names="onnx::Conv_302">
+					<dim>1</dim>
+					<dim>256</dim>
+					<dim>56</dim>
+					<dim>56</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="65" name="onnx::Conv_737" type="Const" version="opset1">
+			<data element_type="f32" shape="256, 256, 3, 3" offset="2831360" size="2359296" />
+			<output>
+				<port id="0" precision="FP32" names="onnx::Conv_737">
+					<dim>256</dim>
+					<dim>256</dim>
+					<dim>3</dim>
+					<dim>3</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="66" name="Conv_50/WithoutBiases" type="Convolution" version="opset1">
+			<data strides="2, 2" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" auto_pad="explicit" />
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>256</dim>
+					<dim>56</dim>
+					<dim>56</dim>
+				</port>
+				<port id="1" precision="FP32">
+					<dim>256</dim>
+					<dim>256</dim>
+					<dim>3</dim>
+					<dim>3</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="FP32">
+					<dim>1</dim>
+					<dim>256</dim>
+					<dim>28</dim>
+					<dim>28</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="67" name="Reshape_711" type="Const" version="opset1">
+			<data element_type="f32" shape="1, 256, 1, 1" offset="5190656" size="1024" />
+			<output>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>256</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="68" name="Conv_50" type="Add" version="opset1">
+			<data auto_broadcast="numpy" />
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>256</dim>
+					<dim>28</dim>
+					<dim>28</dim>
+				</port>
+				<port id="1" precision="FP32">
+					<dim>1</dim>
+					<dim>256</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="FP32" names="input.100">
+					<dim>1</dim>
+					<dim>256</dim>
+					<dim>28</dim>
+					<dim>28</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="69" name="Relu_51" type="ReLU" version="opset1">
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>256</dim>
+					<dim>28</dim>
+					<dim>28</dim>
+				</port>
+			</input>
+			<output>
+				<port id="1" precision="FP32" names="onnx::Conv_305">
+					<dim>1</dim>
+					<dim>256</dim>
+					<dim>28</dim>
+					<dim>28</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="70" name="onnx::Conv_740" type="Const" version="opset1">
+			<data element_type="f32" shape="512, 256, 1, 1" offset="5191680" size="524288" />
+			<output>
+				<port id="0" precision="FP32" names="onnx::Conv_740">
+					<dim>512</dim>
+					<dim>256</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="71" name="Conv_52/WithoutBiases" type="Convolution" version="opset1">
+			<data strides="1, 1" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" auto_pad="explicit" />
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>256</dim>
+					<dim>28</dim>
+					<dim>28</dim>
+				</port>
+				<port id="1" precision="FP32">
+					<dim>512</dim>
+					<dim>256</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="FP32">
+					<dim>1</dim>
+					<dim>512</dim>
+					<dim>28</dim>
+					<dim>28</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="72" name="Reshape_760" type="Const" version="opset1">
+			<data element_type="f32" shape="1, 512, 1, 1" offset="5715968" size="2048" />
+			<output>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>512</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="73" name="Conv_52" type="Add" version="opset1">
+			<data auto_broadcast="numpy" />
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>512</dim>
+					<dim>28</dim>
+					<dim>28</dim>
+				</port>
+				<port id="1" precision="FP32">
+					<dim>1</dim>
+					<dim>512</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="FP32" names="onnx::Add_739">
+					<dim>1</dim>
+					<dim>512</dim>
+					<dim>28</dim>
+					<dim>28</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="74" name="onnx::Conv_743" type="Const" version="opset1">
+			<data element_type="f32" shape="512, 256, 1, 1" offset="5718016" size="524288" />
+			<output>
+				<port id="0" precision="FP32" names="onnx::Conv_743">
+					<dim>512</dim>
+					<dim>256</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="75" name="Conv_53/WithoutBiases" type="Convolution" version="opset1">
+			<data strides="2, 2" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" auto_pad="explicit" />
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>256</dim>
+					<dim>56</dim>
+					<dim>56</dim>
+				</port>
+				<port id="1" precision="FP32">
+					<dim>512</dim>
+					<dim>256</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="FP32">
+					<dim>1</dim>
+					<dim>512</dim>
+					<dim>28</dim>
+					<dim>28</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="76" name="Reshape_808" type="Const" version="opset1">
+			<data element_type="f32" shape="1, 512, 1, 1" offset="6242304" size="2048" />
+			<output>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>512</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="77" name="Conv_53" type="Add" version="opset1">
+			<data auto_broadcast="numpy" />
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>512</dim>
+					<dim>28</dim>
+					<dim>28</dim>
+				</port>
+				<port id="1" precision="FP32">
+					<dim>1</dim>
+					<dim>512</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="FP32" names="onnx::Add_742">
+					<dim>1</dim>
+					<dim>512</dim>
+					<dim>28</dim>
+					<dim>28</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="78" name="Add_54" type="Add" version="opset1">
+			<data auto_broadcast="numpy" />
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>512</dim>
+					<dim>28</dim>
+					<dim>28</dim>
+				</port>
+				<port id="1" precision="FP32">
+					<dim>1</dim>
+					<dim>512</dim>
+					<dim>28</dim>
+					<dim>28</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="FP32" names="input.112">
+					<dim>1</dim>
+					<dim>512</dim>
+					<dim>28</dim>
+					<dim>28</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="79" name="Relu_55" type="ReLU" version="opset1">
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>512</dim>
+					<dim>28</dim>
+					<dim>28</dim>
+				</port>
+			</input>
+			<output>
+				<port id="1" precision="FP32" names="onnx::Conv_311">
+					<dim>1</dim>
+					<dim>512</dim>
+					<dim>28</dim>
+					<dim>28</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="80" name="onnx::Conv_746" type="Const" version="opset1">
+			<data element_type="f32" shape="256, 512, 1, 1" offset="6244352" size="524288" />
+			<output>
+				<port id="0" precision="FP32" names="onnx::Conv_746">
+					<dim>256</dim>
+					<dim>512</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="81" name="Conv_56/WithoutBiases" type="Convolution" version="opset1">
+			<data strides="1, 1" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" auto_pad="explicit" />
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>512</dim>
+					<dim>28</dim>
+					<dim>28</dim>
+				</port>
+				<port id="1" precision="FP32">
+					<dim>256</dim>
+					<dim>512</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="FP32">
+					<dim>1</dim>
+					<dim>256</dim>
+					<dim>28</dim>
+					<dim>28</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="82" name="Reshape_858" type="Const" version="opset1">
+			<data element_type="f32" shape="1, 256, 1, 1" offset="6768640" size="1024" />
+			<output>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>256</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="83" name="Conv_56" type="Add" version="opset1">
+			<data auto_broadcast="numpy" />
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>256</dim>
+					<dim>28</dim>
+					<dim>28</dim>
+				</port>
+				<port id="1" precision="FP32">
+					<dim>1</dim>
+					<dim>256</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="FP32" names="input.120">
+					<dim>1</dim>
+					<dim>256</dim>
+					<dim>28</dim>
+					<dim>28</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="84" name="Relu_57" type="ReLU" version="opset1">
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>256</dim>
+					<dim>28</dim>
+					<dim>28</dim>
+				</port>
+			</input>
+			<output>
+				<port id="1" precision="FP32" names="onnx::Conv_314">
+					<dim>1</dim>
+					<dim>256</dim>
+					<dim>28</dim>
+					<dim>28</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="85" name="onnx::Conv_749" type="Const" version="opset1">
+			<data element_type="f32" shape="256, 256, 3, 3" offset="6769664" size="2359296" />
+			<output>
+				<port id="0" precision="FP32" names="onnx::Conv_749">
+					<dim>256</dim>
+					<dim>256</dim>
+					<dim>3</dim>
+					<dim>3</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="86" name="Conv_58/WithoutBiases" type="Convolution" version="opset1">
+			<data strides="1, 1" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" auto_pad="explicit" />
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>256</dim>
+					<dim>28</dim>
+					<dim>28</dim>
+				</port>
+				<port id="1" precision="FP32">
+					<dim>256</dim>
+					<dim>256</dim>
+					<dim>3</dim>
+					<dim>3</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="FP32">
+					<dim>1</dim>
+					<dim>256</dim>
+					<dim>28</dim>
+					<dim>28</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="87" name="Reshape_907" type="Const" version="opset1">
+			<data element_type="f32" shape="1, 256, 1, 1" offset="9128960" size="1024" />
+			<output>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>256</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="88" name="Conv_58" type="Add" version="opset1">
+			<data auto_broadcast="numpy" />
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>256</dim>
+					<dim>28</dim>
+					<dim>28</dim>
+				</port>
+				<port id="1" precision="FP32">
+					<dim>1</dim>
+					<dim>256</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="FP32" names="input.128">
+					<dim>1</dim>
+					<dim>256</dim>
+					<dim>28</dim>
+					<dim>28</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="89" name="Relu_59" type="ReLU" version="opset1">
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>256</dim>
+					<dim>28</dim>
+					<dim>28</dim>
+				</port>
+			</input>
+			<output>
+				<port id="1" precision="FP32" names="onnx::Conv_317">
+					<dim>1</dim>
+					<dim>256</dim>
+					<dim>28</dim>
+					<dim>28</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="90" name="onnx::Conv_752" type="Const" version="opset1">
+			<data element_type="f32" shape="512, 256, 1, 1" offset="9129984" size="524288" />
+			<output>
+				<port id="0" precision="FP32" names="onnx::Conv_752">
+					<dim>512</dim>
+					<dim>256</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="91" name="Conv_60/WithoutBiases" type="Convolution" version="opset1">
+			<data strides="1, 1" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" auto_pad="explicit" />
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>256</dim>
+					<dim>28</dim>
+					<dim>28</dim>
+				</port>
+				<port id="1" precision="FP32">
+					<dim>512</dim>
+					<dim>256</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="FP32">
+					<dim>1</dim>
+					<dim>512</dim>
+					<dim>28</dim>
+					<dim>28</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="92" name="Reshape_956" type="Const" version="opset1">
+			<data element_type="f32" shape="1, 512, 1, 1" offset="9654272" size="2048" />
+			<output>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>512</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="93" name="Conv_60" type="Add" version="opset1">
+			<data auto_broadcast="numpy" />
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>512</dim>
+					<dim>28</dim>
+					<dim>28</dim>
+				</port>
+				<port id="1" precision="FP32">
+					<dim>1</dim>
+					<dim>512</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="FP32" names="onnx::Add_751">
+					<dim>1</dim>
+					<dim>512</dim>
+					<dim>28</dim>
+					<dim>28</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="94" name="Add_61" type="Add" version="opset1">
+			<data auto_broadcast="numpy" />
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>512</dim>
+					<dim>28</dim>
+					<dim>28</dim>
+				</port>
+				<port id="1" precision="FP32">
+					<dim>1</dim>
+					<dim>512</dim>
+					<dim>28</dim>
+					<dim>28</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="FP32" names="input.136">
+					<dim>1</dim>
+					<dim>512</dim>
+					<dim>28</dim>
+					<dim>28</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="95" name="Relu_62" type="ReLU" version="opset1">
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>512</dim>
+					<dim>28</dim>
+					<dim>28</dim>
+				</port>
+			</input>
+			<output>
+				<port id="1" precision="FP32" names="onnx::Conv_321">
+					<dim>1</dim>
+					<dim>512</dim>
+					<dim>28</dim>
+					<dim>28</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="96" name="onnx::Conv_755" type="Const" version="opset1">
+			<data element_type="f32" shape="256, 512, 1, 1" offset="9656320" size="524288" />
+			<output>
+				<port id="0" precision="FP32" names="onnx::Conv_755">
+					<dim>256</dim>
+					<dim>512</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="97" name="Conv_63/WithoutBiases" type="Convolution" version="opset1">
+			<data strides="1, 1" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" auto_pad="explicit" />
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>512</dim>
+					<dim>28</dim>
+					<dim>28</dim>
+				</port>
+				<port id="1" precision="FP32">
+					<dim>256</dim>
+					<dim>512</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="FP32">
+					<dim>1</dim>
+					<dim>256</dim>
+					<dim>28</dim>
+					<dim>28</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="98" name="Reshape_1006" type="Const" version="opset1">
+			<data element_type="f32" shape="1, 256, 1, 1" offset="10180608" size="1024" />
+			<output>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>256</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="99" name="Conv_63" type="Add" version="opset1">
+			<data auto_broadcast="numpy" />
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>256</dim>
+					<dim>28</dim>
+					<dim>28</dim>
+				</port>
+				<port id="1" precision="FP32">
+					<dim>1</dim>
+					<dim>256</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="FP32" names="input.144">
+					<dim>1</dim>
+					<dim>256</dim>
+					<dim>28</dim>
+					<dim>28</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="100" name="Relu_64" type="ReLU" version="opset1">
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>256</dim>
+					<dim>28</dim>
+					<dim>28</dim>
+				</port>
+			</input>
+			<output>
+				<port id="1" precision="FP32" names="onnx::Conv_324">
+					<dim>1</dim>
+					<dim>256</dim>
+					<dim>28</dim>
+					<dim>28</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="101" name="onnx::Conv_758" type="Const" version="opset1">
+			<data element_type="f32" shape="256, 256, 3, 3" offset="10181632" size="2359296" />
+			<output>
+				<port id="0" precision="FP32" names="onnx::Conv_758">
+					<dim>256</dim>
+					<dim>256</dim>
+					<dim>3</dim>
+					<dim>3</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="102" name="Conv_65/WithoutBiases" type="Convolution" version="opset1">
+			<data strides="1, 1" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" auto_pad="explicit" />
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>256</dim>
+					<dim>28</dim>
+					<dim>28</dim>
+				</port>
+				<port id="1" precision="FP32">
+					<dim>256</dim>
+					<dim>256</dim>
+					<dim>3</dim>
+					<dim>3</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="FP32">
+					<dim>1</dim>
+					<dim>256</dim>
+					<dim>28</dim>
+					<dim>28</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="103" name="Reshape_1055" type="Const" version="opset1">
+			<data element_type="f32" shape="1, 256, 1, 1" offset="12540928" size="1024" />
+			<output>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>256</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="104" name="Conv_65" type="Add" version="opset1">
+			<data auto_broadcast="numpy" />
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>256</dim>
+					<dim>28</dim>
+					<dim>28</dim>
+				</port>
+				<port id="1" precision="FP32">
+					<dim>1</dim>
+					<dim>256</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="FP32" names="input.152">
+					<dim>1</dim>
+					<dim>256</dim>
+					<dim>28</dim>
+					<dim>28</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="105" name="Relu_66" type="ReLU" version="opset1">
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>256</dim>
+					<dim>28</dim>
+					<dim>28</dim>
+				</port>
+			</input>
+			<output>
+				<port id="1" precision="FP32" names="onnx::Conv_327">
+					<dim>1</dim>
+					<dim>256</dim>
+					<dim>28</dim>
+					<dim>28</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="106" name="onnx::Conv_761" type="Const" version="opset1">
+			<data element_type="f32" shape="512, 256, 1, 1" offset="12541952" size="524288" />
+			<output>
+				<port id="0" precision="FP32" names="onnx::Conv_761">
+					<dim>512</dim>
+					<dim>256</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="107" name="Conv_67/WithoutBiases" type="Convolution" version="opset1">
+			<data strides="1, 1" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" auto_pad="explicit" />
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>256</dim>
+					<dim>28</dim>
+					<dim>28</dim>
+				</port>
+				<port id="1" precision="FP32">
+					<dim>512</dim>
+					<dim>256</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="FP32">
+					<dim>1</dim>
+					<dim>512</dim>
+					<dim>28</dim>
+					<dim>28</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="108" name="Reshape_1104" type="Const" version="opset1">
+			<data element_type="f32" shape="1, 512, 1, 1" offset="13066240" size="2048" />
+			<output>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>512</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="109" name="Conv_67" type="Add" version="opset1">
+			<data auto_broadcast="numpy" />
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>512</dim>
+					<dim>28</dim>
+					<dim>28</dim>
+				</port>
+				<port id="1" precision="FP32">
+					<dim>1</dim>
+					<dim>512</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="FP32" names="onnx::Add_760">
+					<dim>1</dim>
+					<dim>512</dim>
+					<dim>28</dim>
+					<dim>28</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="110" name="Add_68" type="Add" version="opset1">
+			<data auto_broadcast="numpy" />
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>512</dim>
+					<dim>28</dim>
+					<dim>28</dim>
+				</port>
+				<port id="1" precision="FP32">
+					<dim>1</dim>
+					<dim>512</dim>
+					<dim>28</dim>
+					<dim>28</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="FP32" names="input.160">
+					<dim>1</dim>
+					<dim>512</dim>
+					<dim>28</dim>
+					<dim>28</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="111" name="Relu_69" type="ReLU" version="opset1">
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>512</dim>
+					<dim>28</dim>
+					<dim>28</dim>
+				</port>
+			</input>
+			<output>
+				<port id="1" precision="FP32" names="onnx::Conv_331">
+					<dim>1</dim>
+					<dim>512</dim>
+					<dim>28</dim>
+					<dim>28</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="112" name="onnx::Conv_764" type="Const" version="opset1">
+			<data element_type="f32" shape="256, 512, 1, 1" offset="13068288" size="524288" />
+			<output>
+				<port id="0" precision="FP32" names="onnx::Conv_764">
+					<dim>256</dim>
+					<dim>512</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="113" name="Conv_70/WithoutBiases" type="Convolution" version="opset1">
+			<data strides="1, 1" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" auto_pad="explicit" />
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>512</dim>
+					<dim>28</dim>
+					<dim>28</dim>
+				</port>
+				<port id="1" precision="FP32">
+					<dim>256</dim>
+					<dim>512</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="FP32">
+					<dim>1</dim>
+					<dim>256</dim>
+					<dim>28</dim>
+					<dim>28</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="114" name="Reshape_1154" type="Const" version="opset1">
+			<data element_type="f32" shape="1, 256, 1, 1" offset="13592576" size="1024" />
+			<output>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>256</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="115" name="Conv_70" type="Add" version="opset1">
+			<data auto_broadcast="numpy" />
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>256</dim>
+					<dim>28</dim>
+					<dim>28</dim>
+				</port>
+				<port id="1" precision="FP32">
+					<dim>1</dim>
+					<dim>256</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="FP32" names="input.168">
+					<dim>1</dim>
+					<dim>256</dim>
+					<dim>28</dim>
+					<dim>28</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="116" name="Relu_71" type="ReLU" version="opset1">
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>256</dim>
+					<dim>28</dim>
+					<dim>28</dim>
+				</port>
+			</input>
+			<output>
+				<port id="1" precision="FP32" names="onnx::Conv_334">
+					<dim>1</dim>
+					<dim>256</dim>
+					<dim>28</dim>
+					<dim>28</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="117" name="onnx::Conv_767" type="Const" version="opset1">
+			<data element_type="f32" shape="256, 256, 3, 3" offset="13593600" size="2359296" />
+			<output>
+				<port id="0" precision="FP32" names="onnx::Conv_767">
+					<dim>256</dim>
+					<dim>256</dim>
+					<dim>3</dim>
+					<dim>3</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="118" name="Conv_72/WithoutBiases" type="Convolution" version="opset1">
+			<data strides="1, 1" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" auto_pad="explicit" />
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>256</dim>
+					<dim>28</dim>
+					<dim>28</dim>
+				</port>
+				<port id="1" precision="FP32">
+					<dim>256</dim>
+					<dim>256</dim>
+					<dim>3</dim>
+					<dim>3</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="FP32">
+					<dim>1</dim>
+					<dim>256</dim>
+					<dim>28</dim>
+					<dim>28</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="119" name="Reshape_1203" type="Const" version="opset1">
+			<data element_type="f32" shape="1, 256, 1, 1" offset="15952896" size="1024" />
+			<output>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>256</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="120" name="Conv_72" type="Add" version="opset1">
+			<data auto_broadcast="numpy" />
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>256</dim>
+					<dim>28</dim>
+					<dim>28</dim>
+				</port>
+				<port id="1" precision="FP32">
+					<dim>1</dim>
+					<dim>256</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="FP32" names="input.176">
+					<dim>1</dim>
+					<dim>256</dim>
+					<dim>28</dim>
+					<dim>28</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="121" name="Relu_73" type="ReLU" version="opset1">
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>256</dim>
+					<dim>28</dim>
+					<dim>28</dim>
+				</port>
+			</input>
+			<output>
+				<port id="1" precision="FP32" names="onnx::Conv_337">
+					<dim>1</dim>
+					<dim>256</dim>
+					<dim>28</dim>
+					<dim>28</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="122" name="onnx::Conv_770" type="Const" version="opset1">
+			<data element_type="f32" shape="512, 256, 1, 1" offset="15953920" size="524288" />
+			<output>
+				<port id="0" precision="FP32" names="onnx::Conv_770">
+					<dim>512</dim>
+					<dim>256</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="123" name="Conv_74/WithoutBiases" type="Convolution" version="opset1">
+			<data strides="1, 1" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" auto_pad="explicit" />
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>256</dim>
+					<dim>28</dim>
+					<dim>28</dim>
+				</port>
+				<port id="1" precision="FP32">
+					<dim>512</dim>
+					<dim>256</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="FP32">
+					<dim>1</dim>
+					<dim>512</dim>
+					<dim>28</dim>
+					<dim>28</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="124" name="Reshape_1252" type="Const" version="opset1">
+			<data element_type="f32" shape="1, 512, 1, 1" offset="16478208" size="2048" />
+			<output>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>512</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="125" name="Conv_74" type="Add" version="opset1">
+			<data auto_broadcast="numpy" />
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>512</dim>
+					<dim>28</dim>
+					<dim>28</dim>
+				</port>
+				<port id="1" precision="FP32">
+					<dim>1</dim>
+					<dim>512</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="FP32" names="onnx::Add_769">
+					<dim>1</dim>
+					<dim>512</dim>
+					<dim>28</dim>
+					<dim>28</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="126" name="Add_75" type="Add" version="opset1">
+			<data auto_broadcast="numpy" />
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>512</dim>
+					<dim>28</dim>
+					<dim>28</dim>
+				</port>
+				<port id="1" precision="FP32">
+					<dim>1</dim>
+					<dim>512</dim>
+					<dim>28</dim>
+					<dim>28</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="FP32" names="input.184">
+					<dim>1</dim>
+					<dim>512</dim>
+					<dim>28</dim>
+					<dim>28</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="127" name="Relu_76" type="ReLU" version="opset1">
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>512</dim>
+					<dim>28</dim>
+					<dim>28</dim>
+				</port>
+			</input>
+			<output>
+				<port id="1" precision="FP32" names="onnx::Conv_341">
+					<dim>1</dim>
+					<dim>512</dim>
+					<dim>28</dim>
+					<dim>28</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="128" name="AveragePool_125" type="AvgPool" version="opset1">
+			<data kernel="3, 3" strides="1, 1" pads_begin="1, 1" pads_end="1, 1" exclude-pad="false" auto_pad="explicit" rounding_type="floor" />
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>512</dim>
+					<dim>28</dim>
+					<dim>28</dim>
+				</port>
+			</input>
+			<output>
+				<port id="1" precision="FP32" names="i">
+					<dim>1</dim>
+					<dim>512</dim>
+					<dim>28</dim>
+					<dim>28</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="129" name="Shape_126" type="ShapeOf" version="opset3">
+			<data output_type="i64" />
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>512</dim>
+					<dim>28</dim>
+					<dim>28</dim>
+				</port>
+			</input>
+			<output>
+				<port id="1" precision="I64" names="onnx::Slice_414">
+					<dim>4</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="130" name="Constant_128" type="Const" version="opset1">
+			<data element_type="i64" shape="1" offset="16480256" size="8" />
+			<output>
+				<port id="0" precision="I64" names="onnx::Slice_416">
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="131" name="Constant_129" type="Const" version="opset1">
+			<data element_type="i64" shape="1" offset="16480264" size="8" />
+			<output>
+				<port id="0" precision="I64" names="onnx::Slice_417">
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="132" name="Broadcast_2236" type="Const" version="opset1">
+			<data element_type="i64" shape="1" offset="16480272" size="8" />
+			<output>
+				<port id="0" precision="I64">
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="133" name="Slice_130" type="StridedSlice" version="opset1">
+			<data begin_mask="0" end_mask="0" new_axis_mask="" shrink_axis_mask="" ellipsis_mask="" />
+			<input>
+				<port id="0" precision="I64">
+					<dim>4</dim>
+				</port>
+				<port id="1" precision="I64">
+					<dim>1</dim>
+				</port>
+				<port id="2" precision="I64">
+					<dim>1</dim>
+				</port>
+				<port id="3" precision="I64">
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="4" precision="I64" names="onnx::Concat_418">
+					<dim>2</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="134" name="onnx::Concat_832" type="Const" version="opset1">
+			<data element_type="i64" shape="2" offset="16480280" size="16" />
+			<output>
+				<port id="0" precision="I64" names="onnx::Concat_832">
+					<dim>2</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="135" name="Concat_131" type="Concat" version="opset1">
+			<data axis="0" />
+			<input>
+				<port id="0" precision="I64">
+					<dim>2</dim>
+				</port>
+				<port id="1" precision="I64">
+					<dim>2</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="I64" names="onnx::Resize_420">
+					<dim>4</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="136" name="Convert_2258" type="Convert" version="opset1">
+			<data destination_type="f32" />
+			<input>
+				<port id="0" precision="I64">
+					<dim>4</dim>
+				</port>
+			</input>
+			<output>
+				<port id="1" precision="FP32">
+					<dim>4</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="137" name="Convert_2257" type="Convert" version="opset1">
+			<data destination_type="f32" />
+			<input>
+				<port id="0" precision="I64">
+					<dim>4</dim>
+				</port>
+			</input>
+			<output>
+				<port id="1" precision="FP32">
+					<dim>4</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="138" name="Divide_2259" type="Divide" version="opset1">
+			<data auto_broadcast="numpy" m_pythondiv="true" />
+			<input>
+				<port id="0" precision="FP32">
+					<dim>4</dim>
+				</port>
+				<port id="1" precision="FP32">
+					<dim>4</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="FP32">
+					<dim>4</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="139" name="Constant_18022" type="Const" version="opset1">
+			<data element_type="f32" shape="1" offset="16480296" size="4" />
+			<output>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="140" name="Add_2261" type="Add" version="opset1">
+			<data auto_broadcast="numpy" />
+			<input>
+				<port id="0" precision="FP32">
+					<dim>4</dim>
+				</port>
+				<port id="1" precision="FP32">
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="FP32">
+					<dim>4</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="141" name="Resize_134" type="Interpolate" version="opset4">
+			<data mode="linear_onnx" shape_calculation_mode="sizes" coordinate_transformation_mode="pytorch_half_pixel" nearest_mode="floor" antialias="false" pads_begin="0, 0, 0, 0" pads_end="0, 0, 0, 0" cube_coeff="-0.75" />
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>512</dim>
+					<dim>28</dim>
+					<dim>28</dim>
+				</port>
+				<port id="1" precision="I64">
+					<dim>4</dim>
+				</port>
+				<port id="2" precision="FP32">
+					<dim>4</dim>
+				</port>
+			</input>
+			<output>
+				<port id="3" precision="FP32" names="onnx::Concat_423">
+					<dim>1</dim>
+					<dim>512</dim>
+					<dim>56</dim>
+					<dim>56</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="142" name="Concat_135" type="Concat" version="opset1">
+			<data axis="1" />
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>256</dim>
+					<dim>56</dim>
+					<dim>56</dim>
+				</port>
+				<port id="1" precision="FP32">
+					<dim>1</dim>
+					<dim>512</dim>
+					<dim>56</dim>
+					<dim>56</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="FP32" names="onnx::Shape_424">
+					<dim>1</dim>
+					<dim>768</dim>
+					<dim>56</dim>
+					<dim>56</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="143" name="onnx::Conv_773" type="Const" version="opset1">
+			<data element_type="f32" shape="512, 512, 1, 1" offset="16480300" size="1048576" />
+			<output>
+				<port id="0" precision="FP32" names="onnx::Conv_773">
+					<dim>512</dim>
+					<dim>512</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="144" name="Conv_77/WithoutBiases" type="Convolution" version="opset1">
+			<data strides="1, 1" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" auto_pad="explicit" />
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>512</dim>
+					<dim>28</dim>
+					<dim>28</dim>
+				</port>
+				<port id="1" precision="FP32">
+					<dim>512</dim>
+					<dim>512</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="FP32">
+					<dim>1</dim>
+					<dim>512</dim>
+					<dim>28</dim>
+					<dim>28</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="145" name="Reshape_1302" type="Const" version="opset1">
+			<data element_type="f32" shape="1, 512, 1, 1" offset="17528876" size="2048" />
+			<output>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>512</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="146" name="Conv_77" type="Add" version="opset1">
+			<data auto_broadcast="numpy" />
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>512</dim>
+					<dim>28</dim>
+					<dim>28</dim>
+				</port>
+				<port id="1" precision="FP32">
+					<dim>1</dim>
+					<dim>512</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="FP32" names="input.192">
+					<dim>1</dim>
+					<dim>512</dim>
+					<dim>28</dim>
+					<dim>28</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="147" name="Relu_78" type="ReLU" version="opset1">
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>512</dim>
+					<dim>28</dim>
+					<dim>28</dim>
+				</port>
+			</input>
+			<output>
+				<port id="1" precision="FP32" names="onnx::Conv_344">
+					<dim>1</dim>
+					<dim>512</dim>
+					<dim>28</dim>
+					<dim>28</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="148" name="onnx::Conv_776" type="Const" version="opset1">
+			<data element_type="f32" shape="512, 512, 3, 3" offset="17530924" size="9437184" />
+			<output>
+				<port id="0" precision="FP32" names="onnx::Conv_776">
+					<dim>512</dim>
+					<dim>512</dim>
+					<dim>3</dim>
+					<dim>3</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="149" name="Conv_79/WithoutBiases" type="Convolution" version="opset1">
+			<data strides="2, 2" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" auto_pad="explicit" />
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>512</dim>
+					<dim>28</dim>
+					<dim>28</dim>
+				</port>
+				<port id="1" precision="FP32">
+					<dim>512</dim>
+					<dim>512</dim>
+					<dim>3</dim>
+					<dim>3</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="FP32">
+					<dim>1</dim>
+					<dim>512</dim>
+					<dim>14</dim>
+					<dim>14</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="150" name="Reshape_1351" type="Const" version="opset1">
+			<data element_type="f32" shape="1, 512, 1, 1" offset="26968108" size="2048" />
+			<output>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>512</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="151" name="Conv_79" type="Add" version="opset1">
+			<data auto_broadcast="numpy" />
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>512</dim>
+					<dim>14</dim>
+					<dim>14</dim>
+				</port>
+				<port id="1" precision="FP32">
+					<dim>1</dim>
+					<dim>512</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="FP32" names="input.200">
+					<dim>1</dim>
+					<dim>512</dim>
+					<dim>14</dim>
+					<dim>14</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="152" name="Relu_80" type="ReLU" version="opset1">
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>512</dim>
+					<dim>14</dim>
+					<dim>14</dim>
+				</port>
+			</input>
+			<output>
+				<port id="1" precision="FP32" names="onnx::Conv_347">
+					<dim>1</dim>
+					<dim>512</dim>
+					<dim>14</dim>
+					<dim>14</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="153" name="onnx::Conv_779" type="Const" version="opset1">
+			<data element_type="f32" shape="1024, 512, 1, 1" offset="26970156" size="2097152" />
+			<output>
+				<port id="0" precision="FP32" names="onnx::Conv_779">
+					<dim>1024</dim>
+					<dim>512</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="154" name="Conv_81/WithoutBiases" type="Convolution" version="opset1">
+			<data strides="1, 1" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" auto_pad="explicit" />
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>512</dim>
+					<dim>14</dim>
+					<dim>14</dim>
+				</port>
+				<port id="1" precision="FP32">
+					<dim>1024</dim>
+					<dim>512</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="FP32">
+					<dim>1</dim>
+					<dim>1024</dim>
+					<dim>14</dim>
+					<dim>14</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="155" name="Reshape_1400" type="Const" version="opset1">
+			<data element_type="f32" shape="1, 1024, 1, 1" offset="29067308" size="4096" />
+			<output>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>1024</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="156" name="Conv_81" type="Add" version="opset1">
+			<data auto_broadcast="numpy" />
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>1024</dim>
+					<dim>14</dim>
+					<dim>14</dim>
+				</port>
+				<port id="1" precision="FP32">
+					<dim>1</dim>
+					<dim>1024</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="FP32" names="onnx::Add_778">
+					<dim>1</dim>
+					<dim>1024</dim>
+					<dim>14</dim>
+					<dim>14</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="157" name="onnx::Conv_782" type="Const" version="opset1">
+			<data element_type="f32" shape="1024, 512, 1, 1" offset="29071404" size="2097152" />
+			<output>
+				<port id="0" precision="FP32" names="onnx::Conv_782">
+					<dim>1024</dim>
+					<dim>512</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="158" name="Conv_82/WithoutBiases" type="Convolution" version="opset1">
+			<data strides="2, 2" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" auto_pad="explicit" />
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>512</dim>
+					<dim>28</dim>
+					<dim>28</dim>
+				</port>
+				<port id="1" precision="FP32">
+					<dim>1024</dim>
+					<dim>512</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="FP32">
+					<dim>1</dim>
+					<dim>1024</dim>
+					<dim>14</dim>
+					<dim>14</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="159" name="Reshape_1448" type="Const" version="opset1">
+			<data element_type="f32" shape="1, 1024, 1, 1" offset="31168556" size="4096" />
+			<output>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>1024</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="160" name="Conv_82" type="Add" version="opset1">
+			<data auto_broadcast="numpy" />
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>1024</dim>
+					<dim>14</dim>
+					<dim>14</dim>
+				</port>
+				<port id="1" precision="FP32">
+					<dim>1</dim>
+					<dim>1024</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="FP32" names="onnx::Add_781">
+					<dim>1</dim>
+					<dim>1024</dim>
+					<dim>14</dim>
+					<dim>14</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="161" name="Add_83" type="Add" version="opset1">
+			<data auto_broadcast="numpy" />
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>1024</dim>
+					<dim>14</dim>
+					<dim>14</dim>
+				</port>
+				<port id="1" precision="FP32">
+					<dim>1</dim>
+					<dim>1024</dim>
+					<dim>14</dim>
+					<dim>14</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="FP32" names="input.212">
+					<dim>1</dim>
+					<dim>1024</dim>
+					<dim>14</dim>
+					<dim>14</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="162" name="Relu_84" type="ReLU" version="opset1">
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>1024</dim>
+					<dim>14</dim>
+					<dim>14</dim>
+				</port>
+			</input>
+			<output>
+				<port id="1" precision="FP32" names="onnx::Conv_353">
+					<dim>1</dim>
+					<dim>1024</dim>
+					<dim>14</dim>
+					<dim>14</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="163" name="onnx::Conv_785" type="Const" version="opset1">
+			<data element_type="f32" shape="512, 1024, 1, 1" offset="31172652" size="2097152" />
+			<output>
+				<port id="0" precision="FP32" names="onnx::Conv_785">
+					<dim>512</dim>
+					<dim>1024</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="164" name="Conv_85/WithoutBiases" type="Convolution" version="opset1">
+			<data strides="1, 1" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" auto_pad="explicit" />
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>1024</dim>
+					<dim>14</dim>
+					<dim>14</dim>
+				</port>
+				<port id="1" precision="FP32">
+					<dim>512</dim>
+					<dim>1024</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="FP32">
+					<dim>1</dim>
+					<dim>512</dim>
+					<dim>14</dim>
+					<dim>14</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="165" name="Reshape_1498" type="Const" version="opset1">
+			<data element_type="f32" shape="1, 512, 1, 1" offset="33269804" size="2048" />
+			<output>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>512</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="166" name="Conv_85" type="Add" version="opset1">
+			<data auto_broadcast="numpy" />
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>512</dim>
+					<dim>14</dim>
+					<dim>14</dim>
+				</port>
+				<port id="1" precision="FP32">
+					<dim>1</dim>
+					<dim>512</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="FP32" names="input.220">
+					<dim>1</dim>
+					<dim>512</dim>
+					<dim>14</dim>
+					<dim>14</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="167" name="Relu_86" type="ReLU" version="opset1">
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>512</dim>
+					<dim>14</dim>
+					<dim>14</dim>
+				</port>
+			</input>
+			<output>
+				<port id="1" precision="FP32" names="onnx::Conv_356">
+					<dim>1</dim>
+					<dim>512</dim>
+					<dim>14</dim>
+					<dim>14</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="168" name="onnx::Conv_788" type="Const" version="opset1">
+			<data element_type="f32" shape="512, 512, 3, 3" offset="33271852" size="9437184" />
+			<output>
+				<port id="0" precision="FP32" names="onnx::Conv_788">
+					<dim>512</dim>
+					<dim>512</dim>
+					<dim>3</dim>
+					<dim>3</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="169" name="Conv_87/WithoutBiases" type="Convolution" version="opset1">
+			<data strides="1, 1" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" auto_pad="explicit" />
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>512</dim>
+					<dim>14</dim>
+					<dim>14</dim>
+				</port>
+				<port id="1" precision="FP32">
+					<dim>512</dim>
+					<dim>512</dim>
+					<dim>3</dim>
+					<dim>3</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="FP32">
+					<dim>1</dim>
+					<dim>512</dim>
+					<dim>14</dim>
+					<dim>14</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="170" name="Reshape_1547" type="Const" version="opset1">
+			<data element_type="f32" shape="1, 512, 1, 1" offset="42709036" size="2048" />
+			<output>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>512</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="171" name="Conv_87" type="Add" version="opset1">
+			<data auto_broadcast="numpy" />
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>512</dim>
+					<dim>14</dim>
+					<dim>14</dim>
+				</port>
+				<port id="1" precision="FP32">
+					<dim>1</dim>
+					<dim>512</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="FP32" names="input.228">
+					<dim>1</dim>
+					<dim>512</dim>
+					<dim>14</dim>
+					<dim>14</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="172" name="Relu_88" type="ReLU" version="opset1">
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>512</dim>
+					<dim>14</dim>
+					<dim>14</dim>
+				</port>
+			</input>
+			<output>
+				<port id="1" precision="FP32" names="onnx::Conv_359">
+					<dim>1</dim>
+					<dim>512</dim>
+					<dim>14</dim>
+					<dim>14</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="173" name="onnx::Conv_791" type="Const" version="opset1">
+			<data element_type="f32" shape="1024, 512, 1, 1" offset="42711084" size="2097152" />
+			<output>
+				<port id="0" precision="FP32" names="onnx::Conv_791">
+					<dim>1024</dim>
+					<dim>512</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="174" name="Conv_89/WithoutBiases" type="Convolution" version="opset1">
+			<data strides="1, 1" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" auto_pad="explicit" />
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>512</dim>
+					<dim>14</dim>
+					<dim>14</dim>
+				</port>
+				<port id="1" precision="FP32">
+					<dim>1024</dim>
+					<dim>512</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="FP32">
+					<dim>1</dim>
+					<dim>1024</dim>
+					<dim>14</dim>
+					<dim>14</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="175" name="Reshape_1596" type="Const" version="opset1">
+			<data element_type="f32" shape="1, 1024, 1, 1" offset="44808236" size="4096" />
+			<output>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>1024</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="176" name="Conv_89" type="Add" version="opset1">
+			<data auto_broadcast="numpy" />
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>1024</dim>
+					<dim>14</dim>
+					<dim>14</dim>
+				</port>
+				<port id="1" precision="FP32">
+					<dim>1</dim>
+					<dim>1024</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="FP32" names="onnx::Add_790">
+					<dim>1</dim>
+					<dim>1024</dim>
+					<dim>14</dim>
+					<dim>14</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="177" name="Add_90" type="Add" version="opset1">
+			<data auto_broadcast="numpy" />
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>1024</dim>
+					<dim>14</dim>
+					<dim>14</dim>
+				</port>
+				<port id="1" precision="FP32">
+					<dim>1</dim>
+					<dim>1024</dim>
+					<dim>14</dim>
+					<dim>14</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="FP32" names="input.236">
+					<dim>1</dim>
+					<dim>1024</dim>
+					<dim>14</dim>
+					<dim>14</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="178" name="Relu_91" type="ReLU" version="opset1">
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>1024</dim>
+					<dim>14</dim>
+					<dim>14</dim>
+				</port>
+			</input>
+			<output>
+				<port id="1" precision="FP32" names="onnx::Conv_363">
+					<dim>1</dim>
+					<dim>1024</dim>
+					<dim>14</dim>
+					<dim>14</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="179" name="onnx::Conv_794" type="Const" version="opset1">
+			<data element_type="f32" shape="512, 1024, 1, 1" offset="44812332" size="2097152" />
+			<output>
+				<port id="0" precision="FP32" names="onnx::Conv_794">
+					<dim>512</dim>
+					<dim>1024</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="180" name="Conv_92/WithoutBiases" type="Convolution" version="opset1">
+			<data strides="1, 1" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" auto_pad="explicit" />
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>1024</dim>
+					<dim>14</dim>
+					<dim>14</dim>
+				</port>
+				<port id="1" precision="FP32">
+					<dim>512</dim>
+					<dim>1024</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="FP32">
+					<dim>1</dim>
+					<dim>512</dim>
+					<dim>14</dim>
+					<dim>14</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="181" name="Reshape_1646" type="Const" version="opset1">
+			<data element_type="f32" shape="1, 512, 1, 1" offset="46909484" size="2048" />
+			<output>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>512</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="182" name="Conv_92" type="Add" version="opset1">
+			<data auto_broadcast="numpy" />
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>512</dim>
+					<dim>14</dim>
+					<dim>14</dim>
+				</port>
+				<port id="1" precision="FP32">
+					<dim>1</dim>
+					<dim>512</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="FP32" names="input.244">
+					<dim>1</dim>
+					<dim>512</dim>
+					<dim>14</dim>
+					<dim>14</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="183" name="Relu_93" type="ReLU" version="opset1">
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>512</dim>
+					<dim>14</dim>
+					<dim>14</dim>
+				</port>
+			</input>
+			<output>
+				<port id="1" precision="FP32" names="onnx::Conv_366">
+					<dim>1</dim>
+					<dim>512</dim>
+					<dim>14</dim>
+					<dim>14</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="184" name="onnx::Conv_797" type="Const" version="opset1">
+			<data element_type="f32" shape="512, 512, 3, 3" offset="46911532" size="9437184" />
+			<output>
+				<port id="0" precision="FP32" names="onnx::Conv_797">
+					<dim>512</dim>
+					<dim>512</dim>
+					<dim>3</dim>
+					<dim>3</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="185" name="Conv_94/WithoutBiases" type="Convolution" version="opset1">
+			<data strides="1, 1" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" auto_pad="explicit" />
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>512</dim>
+					<dim>14</dim>
+					<dim>14</dim>
+				</port>
+				<port id="1" precision="FP32">
+					<dim>512</dim>
+					<dim>512</dim>
+					<dim>3</dim>
+					<dim>3</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="FP32">
+					<dim>1</dim>
+					<dim>512</dim>
+					<dim>14</dim>
+					<dim>14</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="186" name="Reshape_1695" type="Const" version="opset1">
+			<data element_type="f32" shape="1, 512, 1, 1" offset="56348716" size="2048" />
+			<output>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>512</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="187" name="Conv_94" type="Add" version="opset1">
+			<data auto_broadcast="numpy" />
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>512</dim>
+					<dim>14</dim>
+					<dim>14</dim>
+				</port>
+				<port id="1" precision="FP32">
+					<dim>1</dim>
+					<dim>512</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="FP32" names="input.252">
+					<dim>1</dim>
+					<dim>512</dim>
+					<dim>14</dim>
+					<dim>14</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="188" name="Relu_95" type="ReLU" version="opset1">
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>512</dim>
+					<dim>14</dim>
+					<dim>14</dim>
+				</port>
+			</input>
+			<output>
+				<port id="1" precision="FP32" names="onnx::Conv_369">
+					<dim>1</dim>
+					<dim>512</dim>
+					<dim>14</dim>
+					<dim>14</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="189" name="onnx::Conv_800" type="Const" version="opset1">
+			<data element_type="f32" shape="1024, 512, 1, 1" offset="56350764" size="2097152" />
+			<output>
+				<port id="0" precision="FP32" names="onnx::Conv_800">
+					<dim>1024</dim>
+					<dim>512</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="190" name="Conv_96/WithoutBiases" type="Convolution" version="opset1">
+			<data strides="1, 1" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" auto_pad="explicit" />
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>512</dim>
+					<dim>14</dim>
+					<dim>14</dim>
+				</port>
+				<port id="1" precision="FP32">
+					<dim>1024</dim>
+					<dim>512</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="FP32">
+					<dim>1</dim>
+					<dim>1024</dim>
+					<dim>14</dim>
+					<dim>14</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="191" name="Reshape_1744" type="Const" version="opset1">
+			<data element_type="f32" shape="1, 1024, 1, 1" offset="58447916" size="4096" />
+			<output>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>1024</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="192" name="Conv_96" type="Add" version="opset1">
+			<data auto_broadcast="numpy" />
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>1024</dim>
+					<dim>14</dim>
+					<dim>14</dim>
+				</port>
+				<port id="1" precision="FP32">
+					<dim>1</dim>
+					<dim>1024</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="FP32" names="onnx::Add_799">
+					<dim>1</dim>
+					<dim>1024</dim>
+					<dim>14</dim>
+					<dim>14</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="193" name="Add_97" type="Add" version="opset1">
+			<data auto_broadcast="numpy" />
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>1024</dim>
+					<dim>14</dim>
+					<dim>14</dim>
+				</port>
+				<port id="1" precision="FP32">
+					<dim>1</dim>
+					<dim>1024</dim>
+					<dim>14</dim>
+					<dim>14</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="FP32" names="input.260">
+					<dim>1</dim>
+					<dim>1024</dim>
+					<dim>14</dim>
+					<dim>14</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="194" name="Relu_98" type="ReLU" version="opset1">
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>1024</dim>
+					<dim>14</dim>
+					<dim>14</dim>
+				</port>
+			</input>
+			<output>
+				<port id="1" precision="FP32" names="onnx::Conv_373">
+					<dim>1</dim>
+					<dim>1024</dim>
+					<dim>14</dim>
+					<dim>14</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="195" name="onnx::Conv_803" type="Const" version="opset1">
+			<data element_type="f32" shape="512, 1024, 1, 1" offset="58452012" size="2097152" />
+			<output>
+				<port id="0" precision="FP32" names="onnx::Conv_803">
+					<dim>512</dim>
+					<dim>1024</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="196" name="Conv_99/WithoutBiases" type="Convolution" version="opset1">
+			<data strides="1, 1" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" auto_pad="explicit" />
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>1024</dim>
+					<dim>14</dim>
+					<dim>14</dim>
+				</port>
+				<port id="1" precision="FP32">
+					<dim>512</dim>
+					<dim>1024</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="FP32">
+					<dim>1</dim>
+					<dim>512</dim>
+					<dim>14</dim>
+					<dim>14</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="197" name="Reshape_1794" type="Const" version="opset1">
+			<data element_type="f32" shape="1, 512, 1, 1" offset="60549164" size="2048" />
+			<output>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>512</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="198" name="Conv_99" type="Add" version="opset1">
+			<data auto_broadcast="numpy" />
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>512</dim>
+					<dim>14</dim>
+					<dim>14</dim>
+				</port>
+				<port id="1" precision="FP32">
+					<dim>1</dim>
+					<dim>512</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="FP32" names="input.268">
+					<dim>1</dim>
+					<dim>512</dim>
+					<dim>14</dim>
+					<dim>14</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="199" name="Relu_100" type="ReLU" version="opset1">
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>512</dim>
+					<dim>14</dim>
+					<dim>14</dim>
+				</port>
+			</input>
+			<output>
+				<port id="1" precision="FP32" names="onnx::Conv_376">
+					<dim>1</dim>
+					<dim>512</dim>
+					<dim>14</dim>
+					<dim>14</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="200" name="onnx::Conv_806" type="Const" version="opset1">
+			<data element_type="f32" shape="512, 512, 3, 3" offset="60551212" size="9437184" />
+			<output>
+				<port id="0" precision="FP32" names="onnx::Conv_806">
+					<dim>512</dim>
+					<dim>512</dim>
+					<dim>3</dim>
+					<dim>3</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="201" name="Conv_101/WithoutBiases" type="Convolution" version="opset1">
+			<data strides="1, 1" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" auto_pad="explicit" />
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>512</dim>
+					<dim>14</dim>
+					<dim>14</dim>
+				</port>
+				<port id="1" precision="FP32">
+					<dim>512</dim>
+					<dim>512</dim>
+					<dim>3</dim>
+					<dim>3</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="FP32">
+					<dim>1</dim>
+					<dim>512</dim>
+					<dim>14</dim>
+					<dim>14</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="202" name="Reshape_1843" type="Const" version="opset1">
+			<data element_type="f32" shape="1, 512, 1, 1" offset="69988396" size="2048" />
+			<output>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>512</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="203" name="Conv_101" type="Add" version="opset1">
+			<data auto_broadcast="numpy" />
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>512</dim>
+					<dim>14</dim>
+					<dim>14</dim>
+				</port>
+				<port id="1" precision="FP32">
+					<dim>1</dim>
+					<dim>512</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="FP32" names="input.276">
+					<dim>1</dim>
+					<dim>512</dim>
+					<dim>14</dim>
+					<dim>14</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="204" name="Relu_102" type="ReLU" version="opset1">
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>512</dim>
+					<dim>14</dim>
+					<dim>14</dim>
+				</port>
+			</input>
+			<output>
+				<port id="1" precision="FP32" names="onnx::Conv_379">
+					<dim>1</dim>
+					<dim>512</dim>
+					<dim>14</dim>
+					<dim>14</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="205" name="onnx::Conv_809" type="Const" version="opset1">
+			<data element_type="f32" shape="1024, 512, 1, 1" offset="69990444" size="2097152" />
+			<output>
+				<port id="0" precision="FP32" names="onnx::Conv_809">
+					<dim>1024</dim>
+					<dim>512</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="206" name="Conv_103/WithoutBiases" type="Convolution" version="opset1">
+			<data strides="1, 1" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" auto_pad="explicit" />
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>512</dim>
+					<dim>14</dim>
+					<dim>14</dim>
+				</port>
+				<port id="1" precision="FP32">
+					<dim>1024</dim>
+					<dim>512</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="FP32">
+					<dim>1</dim>
+					<dim>1024</dim>
+					<dim>14</dim>
+					<dim>14</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="207" name="Reshape_1892" type="Const" version="opset1">
+			<data element_type="f32" shape="1, 1024, 1, 1" offset="72087596" size="4096" />
+			<output>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>1024</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="208" name="Conv_103" type="Add" version="opset1">
+			<data auto_broadcast="numpy" />
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>1024</dim>
+					<dim>14</dim>
+					<dim>14</dim>
+				</port>
+				<port id="1" precision="FP32">
+					<dim>1</dim>
+					<dim>1024</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="FP32" names="onnx::Add_808">
+					<dim>1</dim>
+					<dim>1024</dim>
+					<dim>14</dim>
+					<dim>14</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="209" name="Add_104" type="Add" version="opset1">
+			<data auto_broadcast="numpy" />
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>1024</dim>
+					<dim>14</dim>
+					<dim>14</dim>
+				</port>
+				<port id="1" precision="FP32">
+					<dim>1</dim>
+					<dim>1024</dim>
+					<dim>14</dim>
+					<dim>14</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="FP32" names="input.284">
+					<dim>1</dim>
+					<dim>1024</dim>
+					<dim>14</dim>
+					<dim>14</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="210" name="Relu_105" type="ReLU" version="opset1">
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>1024</dim>
+					<dim>14</dim>
+					<dim>14</dim>
+				</port>
+			</input>
+			<output>
+				<port id="1" precision="FP32" names="onnx::Conv_383">
+					<dim>1</dim>
+					<dim>1024</dim>
+					<dim>14</dim>
+					<dim>14</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="211" name="onnx::Conv_812" type="Const" version="opset1">
+			<data element_type="f32" shape="512, 1024, 1, 1" offset="72091692" size="2097152" />
+			<output>
+				<port id="0" precision="FP32" names="onnx::Conv_812">
+					<dim>512</dim>
+					<dim>1024</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="212" name="Conv_106/WithoutBiases" type="Convolution" version="opset1">
+			<data strides="1, 1" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" auto_pad="explicit" />
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>1024</dim>
+					<dim>14</dim>
+					<dim>14</dim>
+				</port>
+				<port id="1" precision="FP32">
+					<dim>512</dim>
+					<dim>1024</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="FP32">
+					<dim>1</dim>
+					<dim>512</dim>
+					<dim>14</dim>
+					<dim>14</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="213" name="Reshape_1942" type="Const" version="opset1">
+			<data element_type="f32" shape="1, 512, 1, 1" offset="74188844" size="2048" />
+			<output>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>512</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="214" name="Conv_106" type="Add" version="opset1">
+			<data auto_broadcast="numpy" />
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>512</dim>
+					<dim>14</dim>
+					<dim>14</dim>
+				</port>
+				<port id="1" precision="FP32">
+					<dim>1</dim>
+					<dim>512</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="FP32" names="input.292">
+					<dim>1</dim>
+					<dim>512</dim>
+					<dim>14</dim>
+					<dim>14</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="215" name="Relu_107" type="ReLU" version="opset1">
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>512</dim>
+					<dim>14</dim>
+					<dim>14</dim>
+				</port>
+			</input>
+			<output>
+				<port id="1" precision="FP32" names="onnx::Conv_386">
+					<dim>1</dim>
+					<dim>512</dim>
+					<dim>14</dim>
+					<dim>14</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="216" name="onnx::Conv_815" type="Const" version="opset1">
+			<data element_type="f32" shape="512, 512, 3, 3" offset="74190892" size="9437184" />
+			<output>
+				<port id="0" precision="FP32" names="onnx::Conv_815">
+					<dim>512</dim>
+					<dim>512</dim>
+					<dim>3</dim>
+					<dim>3</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="217" name="Conv_108/WithoutBiases" type="Convolution" version="opset1">
+			<data strides="1, 1" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" auto_pad="explicit" />
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>512</dim>
+					<dim>14</dim>
+					<dim>14</dim>
+				</port>
+				<port id="1" precision="FP32">
+					<dim>512</dim>
+					<dim>512</dim>
+					<dim>3</dim>
+					<dim>3</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="FP32">
+					<dim>1</dim>
+					<dim>512</dim>
+					<dim>14</dim>
+					<dim>14</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="218" name="Reshape_1991" type="Const" version="opset1">
+			<data element_type="f32" shape="1, 512, 1, 1" offset="83628076" size="2048" />
+			<output>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>512</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="219" name="Conv_108" type="Add" version="opset1">
+			<data auto_broadcast="numpy" />
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>512</dim>
+					<dim>14</dim>
+					<dim>14</dim>
+				</port>
+				<port id="1" precision="FP32">
+					<dim>1</dim>
+					<dim>512</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="FP32" names="input.300">
+					<dim>1</dim>
+					<dim>512</dim>
+					<dim>14</dim>
+					<dim>14</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="220" name="Relu_109" type="ReLU" version="opset1">
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>512</dim>
+					<dim>14</dim>
+					<dim>14</dim>
+				</port>
+			</input>
+			<output>
+				<port id="1" precision="FP32" names="onnx::Conv_389">
+					<dim>1</dim>
+					<dim>512</dim>
+					<dim>14</dim>
+					<dim>14</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="221" name="onnx::Conv_818" type="Const" version="opset1">
+			<data element_type="f32" shape="1024, 512, 1, 1" offset="83630124" size="2097152" />
+			<output>
+				<port id="0" precision="FP32" names="onnx::Conv_818">
+					<dim>1024</dim>
+					<dim>512</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="222" name="Conv_110/WithoutBiases" type="Convolution" version="opset1">
+			<data strides="1, 1" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" auto_pad="explicit" />
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>512</dim>
+					<dim>14</dim>
+					<dim>14</dim>
+				</port>
+				<port id="1" precision="FP32">
+					<dim>1024</dim>
+					<dim>512</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="FP32">
+					<dim>1</dim>
+					<dim>1024</dim>
+					<dim>14</dim>
+					<dim>14</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="223" name="Reshape_2040" type="Const" version="opset1">
+			<data element_type="f32" shape="1, 1024, 1, 1" offset="85727276" size="4096" />
+			<output>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>1024</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="224" name="Conv_110" type="Add" version="opset1">
+			<data auto_broadcast="numpy" />
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>1024</dim>
+					<dim>14</dim>
+					<dim>14</dim>
+				</port>
+				<port id="1" precision="FP32">
+					<dim>1</dim>
+					<dim>1024</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="FP32" names="onnx::Add_817">
+					<dim>1</dim>
+					<dim>1024</dim>
+					<dim>14</dim>
+					<dim>14</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="225" name="Add_111" type="Add" version="opset1">
+			<data auto_broadcast="numpy" />
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>1024</dim>
+					<dim>14</dim>
+					<dim>14</dim>
+				</port>
+				<port id="1" precision="FP32">
+					<dim>1</dim>
+					<dim>1024</dim>
+					<dim>14</dim>
+					<dim>14</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="FP32" names="input.308">
+					<dim>1</dim>
+					<dim>1024</dim>
+					<dim>14</dim>
+					<dim>14</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="226" name="Relu_112" type="ReLU" version="opset1">
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>1024</dim>
+					<dim>14</dim>
+					<dim>14</dim>
+				</port>
+			</input>
+			<output>
+				<port id="1" precision="FP32" names="onnx::Conv_393">
+					<dim>1</dim>
+					<dim>1024</dim>
+					<dim>14</dim>
+					<dim>14</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="227" name="onnx::Conv_821" type="Const" version="opset1">
+			<data element_type="f32" shape="512, 1024, 1, 1" offset="85731372" size="2097152" />
+			<output>
+				<port id="0" precision="FP32" names="onnx::Conv_821">
+					<dim>512</dim>
+					<dim>1024</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="228" name="Conv_113/WithoutBiases" type="Convolution" version="opset1">
+			<data strides="1, 1" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" auto_pad="explicit" />
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>1024</dim>
+					<dim>14</dim>
+					<dim>14</dim>
+				</port>
+				<port id="1" precision="FP32">
+					<dim>512</dim>
+					<dim>1024</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="FP32">
+					<dim>1</dim>
+					<dim>512</dim>
+					<dim>14</dim>
+					<dim>14</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="229" name="Reshape_2090" type="Const" version="opset1">
+			<data element_type="f32" shape="1, 512, 1, 1" offset="87828524" size="2048" />
+			<output>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>512</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="230" name="Conv_113" type="Add" version="opset1">
+			<data auto_broadcast="numpy" />
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>512</dim>
+					<dim>14</dim>
+					<dim>14</dim>
+				</port>
+				<port id="1" precision="FP32">
+					<dim>1</dim>
+					<dim>512</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="FP32" names="input.316">
+					<dim>1</dim>
+					<dim>512</dim>
+					<dim>14</dim>
+					<dim>14</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="231" name="Relu_114" type="ReLU" version="opset1">
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>512</dim>
+					<dim>14</dim>
+					<dim>14</dim>
+				</port>
+			</input>
+			<output>
+				<port id="1" precision="FP32" names="onnx::Conv_396">
+					<dim>1</dim>
+					<dim>512</dim>
+					<dim>14</dim>
+					<dim>14</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="232" name="onnx::Conv_824" type="Const" version="opset1">
+			<data element_type="f32" shape="512, 512, 3, 3" offset="87830572" size="9437184" />
+			<output>
+				<port id="0" precision="FP32" names="onnx::Conv_824">
+					<dim>512</dim>
+					<dim>512</dim>
+					<dim>3</dim>
+					<dim>3</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="233" name="Conv_115/WithoutBiases" type="Convolution" version="opset1">
+			<data strides="1, 1" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" auto_pad="explicit" />
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>512</dim>
+					<dim>14</dim>
+					<dim>14</dim>
+				</port>
+				<port id="1" precision="FP32">
+					<dim>512</dim>
+					<dim>512</dim>
+					<dim>3</dim>
+					<dim>3</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="FP32">
+					<dim>1</dim>
+					<dim>512</dim>
+					<dim>14</dim>
+					<dim>14</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="234" name="Reshape_2139" type="Const" version="opset1">
+			<data element_type="f32" shape="1, 512, 1, 1" offset="97267756" size="2048" />
+			<output>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>512</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="235" name="Conv_115" type="Add" version="opset1">
+			<data auto_broadcast="numpy" />
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>512</dim>
+					<dim>14</dim>
+					<dim>14</dim>
+				</port>
+				<port id="1" precision="FP32">
+					<dim>1</dim>
+					<dim>512</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="FP32" names="input.324">
+					<dim>1</dim>
+					<dim>512</dim>
+					<dim>14</dim>
+					<dim>14</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="236" name="Relu_116" type="ReLU" version="opset1">
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>512</dim>
+					<dim>14</dim>
+					<dim>14</dim>
+				</port>
+			</input>
+			<output>
+				<port id="1" precision="FP32" names="onnx::Conv_399">
+					<dim>1</dim>
+					<dim>512</dim>
+					<dim>14</dim>
+					<dim>14</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="237" name="onnx::Conv_827" type="Const" version="opset1">
+			<data element_type="f32" shape="1024, 512, 1, 1" offset="97269804" size="2097152" />
+			<output>
+				<port id="0" precision="FP32" names="onnx::Conv_827">
+					<dim>1024</dim>
+					<dim>512</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="238" name="Conv_117/WithoutBiases" type="Convolution" version="opset1">
+			<data strides="1, 1" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" auto_pad="explicit" />
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>512</dim>
+					<dim>14</dim>
+					<dim>14</dim>
+				</port>
+				<port id="1" precision="FP32">
+					<dim>1024</dim>
+					<dim>512</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="FP32">
+					<dim>1</dim>
+					<dim>1024</dim>
+					<dim>14</dim>
+					<dim>14</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="239" name="Reshape_2188" type="Const" version="opset1">
+			<data element_type="f32" shape="1, 1024, 1, 1" offset="99366956" size="4096" />
+			<output>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>1024</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="240" name="Conv_117" type="Add" version="opset1">
+			<data auto_broadcast="numpy" />
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>1024</dim>
+					<dim>14</dim>
+					<dim>14</dim>
+				</port>
+				<port id="1" precision="FP32">
+					<dim>1</dim>
+					<dim>1024</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="FP32" names="onnx::Add_826">
+					<dim>1</dim>
+					<dim>1024</dim>
+					<dim>14</dim>
+					<dim>14</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="241" name="Add_118" type="Add" version="opset1">
+			<data auto_broadcast="numpy" />
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>1024</dim>
+					<dim>14</dim>
+					<dim>14</dim>
+				</port>
+				<port id="1" precision="FP32">
+					<dim>1</dim>
+					<dim>1024</dim>
+					<dim>14</dim>
+					<dim>14</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="FP32" names="input.332">
+					<dim>1</dim>
+					<dim>1024</dim>
+					<dim>14</dim>
+					<dim>14</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="242" name="Relu_119" type="ReLU" version="opset1">
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>1024</dim>
+					<dim>14</dim>
+					<dim>14</dim>
+				</port>
+			</input>
+			<output>
+				<port id="1" precision="FP32" names="onnx::Pad_403">
+					<dim>1</dim>
+					<dim>1024</dim>
+					<dim>14</dim>
+					<dim>14</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="243" name="AveragePool_138" type="AvgPool" version="opset1">
+			<data kernel="3, 3" strides="1, 1" pads_begin="1, 1" pads_end="1, 1" exclude-pad="false" auto_pad="explicit" rounding_type="floor" />
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>1024</dim>
+					<dim>14</dim>
+					<dim>14</dim>
+				</port>
+			</input>
+			<output>
+				<port id="1" precision="FP32" names="i.3">
+					<dim>1</dim>
+					<dim>1024</dim>
+					<dim>14</dim>
+					<dim>14</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="244" name="Shape_145" type="ShapeOf" version="opset3">
+			<data output_type="i64" />
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>1024</dim>
+					<dim>14</dim>
+					<dim>14</dim>
+				</port>
+			</input>
+			<output>
+				<port id="1" precision="I64" names="onnx::Slice_434">
+					<dim>4</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="245" name="Constant_147" type="Const" version="opset1">
+			<data element_type="i64" shape="1" offset="16480256" size="8" />
+			<output>
+				<port id="0" precision="I64" names="onnx::Slice_436">
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="246" name="Constant_148" type="Const" version="opset1">
+			<data element_type="i64" shape="1" offset="16480264" size="8" />
+			<output>
+				<port id="0" precision="I64" names="onnx::Slice_437">
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="247" name="Broadcast_2317" type="Const" version="opset1">
+			<data element_type="i64" shape="1" offset="16480272" size="8" />
+			<output>
+				<port id="0" precision="I64">
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="248" name="Slice_149" type="StridedSlice" version="opset1">
+			<data begin_mask="0" end_mask="0" new_axis_mask="" shrink_axis_mask="" ellipsis_mask="" />
+			<input>
+				<port id="0" precision="I64">
+					<dim>4</dim>
+				</port>
+				<port id="1" precision="I64">
+					<dim>1</dim>
+				</port>
+				<port id="2" precision="I64">
+					<dim>1</dim>
+				</port>
+				<port id="3" precision="I64">
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="4" precision="I64" names="onnx::Concat_438">
+					<dim>2</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="249" name="Shape_139" type="ShapeOf" version="opset3">
+			<data output_type="i64" />
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>768</dim>
+					<dim>56</dim>
+					<dim>56</dim>
+				</port>
+			</input>
+			<output>
+				<port id="1" precision="I64" names="onnx::Gather_428">
+					<dim>4</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="250" name="Constant_16175" type="Const" version="opset1">
+			<data element_type="i64" shape="2" offset="99371052" size="16" />
+			<output>
+				<port id="0" precision="I64">
+					<dim>2</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="251" name="Constant_16176" type="Const" version="opset1">
+			<data element_type="i64" shape="" offset="16480256" size="8" />
+			<output>
+				<port id="0" precision="I64" />
+			</output>
+		</layer>
+		<layer id="252" name="Gather_16177" type="Gather" version="opset8">
+			<data batch_dims="0" />
+			<input>
+				<port id="0" precision="I64">
+					<dim>4</dim>
+				</port>
+				<port id="1" precision="I64">
+					<dim>2</dim>
+				</port>
+				<port id="2" precision="I64" />
+			</input>
+			<output>
+				<port id="3" precision="I64" names="onnx::Cast_433,onnx::Concat_439">
+					<dim>2</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="253" name="Concat_151" type="Concat" version="opset1">
+			<data axis="0" />
+			<input>
+				<port id="0" precision="I64">
+					<dim>2</dim>
+				</port>
+				<port id="1" precision="I64">
+					<dim>2</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="I64" names="onnx::Resize_440">
+					<dim>4</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="254" name="Convert_2340" type="Convert" version="opset1">
+			<data destination_type="f32" />
+			<input>
+				<port id="0" precision="I64">
+					<dim>4</dim>
+				</port>
+			</input>
+			<output>
+				<port id="1" precision="FP32">
+					<dim>4</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="255" name="Convert_2339" type="Convert" version="opset1">
+			<data destination_type="f32" />
+			<input>
+				<port id="0" precision="I64">
+					<dim>4</dim>
+				</port>
+			</input>
+			<output>
+				<port id="1" precision="FP32">
+					<dim>4</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="256" name="Divide_2341" type="Divide" version="opset1">
+			<data auto_broadcast="numpy" m_pythondiv="true" />
+			<input>
+				<port id="0" precision="FP32">
+					<dim>4</dim>
+				</port>
+				<port id="1" precision="FP32">
+					<dim>4</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="FP32">
+					<dim>4</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="257" name="Constant_18023" type="Const" version="opset1">
+			<data element_type="f32" shape="1" offset="16480296" size="4" />
+			<output>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="258" name="Add_2343" type="Add" version="opset1">
+			<data auto_broadcast="numpy" />
+			<input>
+				<port id="0" precision="FP32">
+					<dim>4</dim>
+				</port>
+				<port id="1" precision="FP32">
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="FP32">
+					<dim>4</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="259" name="Resize_154" type="Interpolate" version="opset4">
+			<data mode="linear_onnx" shape_calculation_mode="sizes" coordinate_transformation_mode="pytorch_half_pixel" nearest_mode="floor" antialias="false" pads_begin="0, 0, 0, 0" pads_end="0, 0, 0, 0" cube_coeff="-0.75" />
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>1024</dim>
+					<dim>14</dim>
+					<dim>14</dim>
+				</port>
+				<port id="1" precision="I64">
+					<dim>4</dim>
+				</port>
+				<port id="2" precision="FP32">
+					<dim>4</dim>
+				</port>
+			</input>
+			<output>
+				<port id="3" precision="FP32" names="onnx::Concat_443">
+					<dim>1</dim>
+					<dim>1024</dim>
+					<dim>56</dim>
+					<dim>56</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="260" name="Concat_155" type="Concat" version="opset1">
+			<data axis="1" />
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>768</dim>
+					<dim>56</dim>
+					<dim>56</dim>
+				</port>
+				<port id="1" precision="FP32">
+					<dim>1</dim>
+					<dim>1024</dim>
+					<dim>56</dim>
+					<dim>56</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="FP32" names="onnx::Shape_444">
+					<dim>1</dim>
+					<dim>1792</dim>
+					<dim>56</dim>
+					<dim>56</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="261" name="Constant_172" type="Const" version="opset1">
+			<data element_type="i32" shape="" offset="99371068" size="4" />
+			<output>
+				<port id="0" precision="I32" names="onnx::Range_473" />
+			</output>
+		</layer>
+		<layer id="262" name="Shape_159" type="ShapeOf" version="opset3">
+			<data output_type="i64" />
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>1792</dim>
+					<dim>56</dim>
+					<dim>56</dim>
+				</port>
+			</input>
+			<output>
+				<port id="1" precision="I64" names="onnx::Gather_445,onnx::Gather_448,onnx::Gather_451">
+					<dim>4</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="263" name="Constant_160" type="Const" version="opset1">
+			<data element_type="i64" shape="" offset="16480264" size="8" />
+			<output>
+				<port id="0" precision="I64" names="onnx::Gather_449" />
+			</output>
+		</layer>
+		<layer id="264" name="Constant_2427" type="Const" version="opset1">
+			<data element_type="i64" shape="" offset="16480256" size="8" />
+			<output>
+				<port id="0" precision="I64" />
+			</output>
+		</layer>
+		<layer id="265" name="Gather_161" type="Gather" version="opset8">
+			<data batch_dims="0" />
+			<input>
+				<port id="0" precision="I64">
+					<dim>4</dim>
+				</port>
+				<port id="1" precision="I64" />
+				<port id="2" precision="I64" />
+			</input>
+			<output>
+				<port id="3" precision="I64" names="onnx::Unsqueeze_450" />
+			</output>
+		</layer>
+		<layer id="266" name="Cast_171" type="Convert" version="opset1">
+			<data destination_type="i32" />
+			<input>
+				<port id="0" precision="I64" />
+			</input>
+			<output>
+				<port id="1" precision="I32" names="onnx::Range_472" />
+			</output>
+		</layer>
+		<layer id="267" name="Constant_173" type="Const" version="opset1">
+			<data element_type="i32" shape="" offset="99371072" size="4" />
+			<output>
+				<port id="0" precision="I32" names="onnx::Range_474" />
+			</output>
+		</layer>
+		<layer id="268" name="Range_174" type="Range" version="opset4">
+			<data output_type="i32" />
+			<input>
+				<port id="0" precision="I32" />
+				<port id="1" precision="I32" />
+				<port id="2" precision="I32" />
+			</input>
+			<output>
+				<port id="3" precision="I32" names="onnx::Unsqueeze_475">
+					<dim>56</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="269" name="Constant_2557" type="Const" version="opset1">
+			<data element_type="i64" shape="1" offset="16480256" size="8" />
+			<output>
+				<port id="0" precision="I64">
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="270" name="Unsqueeze_179" type="Unsqueeze" version="opset1">
+			<input>
+				<port id="0" precision="I32">
+					<dim>56</dim>
+				</port>
+				<port id="1" precision="I64">
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="I32" names="onnx::Unsqueeze_480">
+					<dim>1</dim>
+					<dim>56</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="271" name="Constant_2559" type="Const" version="opset1">
+			<data element_type="i64" shape="1" offset="16480272" size="8" />
+			<output>
+				<port id="0" precision="I64">
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="272" name="Unsqueeze_180" type="Unsqueeze" version="opset1">
+			<input>
+				<port id="0" precision="I32">
+					<dim>1</dim>
+					<dim>56</dim>
+				</port>
+				<port id="1" precision="I64">
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="I32" names="onnx::Unsqueeze_481">
+					<dim>1</dim>
+					<dim>1</dim>
+					<dim>56</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="273" name="Constant_2561" type="Const" version="opset1">
+			<data element_type="i64" shape="1" offset="99371076" size="8" />
+			<output>
+				<port id="0" precision="I64">
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="274" name="Unsqueeze_181" type="Unsqueeze" version="opset1">
+			<input>
+				<port id="0" precision="I32">
+					<dim>1</dim>
+					<dim>1</dim>
+					<dim>56</dim>
+				</port>
+				<port id="1" precision="I64">
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="I32" names="onnx::MatMul_482">
+					<dim>1</dim>
+					<dim>1</dim>
+					<dim>56</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="275" name="Constant_2437" type="Const" version="opset1">
+			<data element_type="i32" shape="" offset="99371072" size="4" />
+			<output>
+				<port id="0" precision="I32" />
+			</output>
+		</layer>
+		<layer id="276" name="onnx::Concat_842" type="Const" version="opset1">
+			<data element_type="i64" shape="1" offset="16480272" size="8" />
+			<output>
+				<port id="0" precision="I64" names="onnx::Concat_833,onnx::Concat_834,onnx::Concat_835,onnx::Concat_836,onnx::Concat_837,onnx::Concat_838,onnx::Concat_839,onnx::Concat_840,onnx::Concat_841,onnx::Concat_842,onnx::Concat_843,onnx::Concat_844,onnx::Concat_845,onnx::Concat_846,onnx::Concat_847,onnx::Concat_848,onnx::Concat_849,onnx::Concat_850">
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="277" name="Constant_16109" type="Const" version="opset1">
+			<data element_type="i64" shape="1" offset="99371076" size="8" />
+			<output>
+				<port id="0" precision="I64">
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="278" name="Constant_2431" type="Const" version="opset1">
+			<data element_type="i64" shape="" offset="16480256" size="8" />
+			<output>
+				<port id="0" precision="I64" />
+			</output>
+		</layer>
+		<layer id="279" name="Gather_164" type="Gather" version="opset8">
+			<data batch_dims="0" />
+			<input>
+				<port id="0" precision="I64">
+					<dim>4</dim>
+				</port>
+				<port id="1" precision="I64">
+					<dim>1</dim>
+				</port>
+				<port id="2" precision="I64" />
+			</input>
+			<output>
+				<port id="3" precision="I64" names="onnx::Concat_460">
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="280" name="Concat_166" type="Concat" version="opset1">
+			<data axis="0" />
+			<input>
+				<port id="0" precision="I64">
+					<dim>1</dim>
+				</port>
+				<port id="1" precision="I64">
+					<dim>1</dim>
+				</port>
+				<port id="2" precision="I64">
+					<dim>1</dim>
+				</port>
+				<port id="3" precision="I64">
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="4" precision="I64" names="onnx::ConstantOfShape_461">
+					<dim>4</dim>
+				</port>
+			</output>
+		</layer>
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+					<dim>56</dim>
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+			</output>
+		</layer>
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+			</input>
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+			</output>
+		</layer>
+		<layer id="283" name="Cast_188" type="Convert" version="opset1">
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+				<port id="0" precision="I32">
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+			</input>
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+			</output>
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+			<output>
+				<port id="0" precision="I64" names="onnx::Sub_490" />
+			</output>
+		</layer>
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+			<data auto_broadcast="numpy" />
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+				<port id="0" precision="I64" />
+				<port id="1" precision="I64" />
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+			<output>
+				<port id="2" precision="I64" names="onnx::Cast_491" />
+			</output>
+		</layer>
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+			<data destination_type="f32" />
+			<input>
+				<port id="0" precision="I64" />
+			</input>
+			<output>
+				<port id="1" precision="FP32" names="onnx::Div_492" />
+			</output>
+		</layer>
+		<layer id="287" name="Div_192" type="Divide" version="opset1">
+			<data auto_broadcast="numpy" m_pythondiv="true" />
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+				<port id="0" precision="FP32">
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+			</output>
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+			<data auto_broadcast="numpy" />
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+			</output>
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+			<data auto_broadcast="numpy" />
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+				<port id="0" precision="FP32">
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+			</input>
+			<output>
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+			</output>
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+			<output>
+				<port id="0" precision="I64" names="onnx::Expand_521">
+					<dim>4</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="293" name="Expand_212" type="Broadcast" version="opset3">
+			<data mode="bidirectional" />
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+				<port id="0" precision="FP32">
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+			<output>
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+			</output>
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+				<port id="0" precision="I64" />
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+				<port id="0" precision="I64">
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+			</output>
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+			</output>
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+			<output>
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+				<port id="0" precision="I64">
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+				<port id="2" precision="I64" />
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+				<port id="0" precision="I64" />
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+				<port id="0" precision="I32" names="onnx::Range_478" />
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+				<port id="1" precision="I32" />
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+				<port id="0" precision="I32" />
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+				<port id="0" precision="I32" />
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+			<data transpose_a="false" transpose_b="false" />
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+			<data destination_type="f32" />
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+				<port id="0" precision="FP32">
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+				<port id="1" precision="I64">
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+				<port id="0" precision="I64" />
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+			<output>
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+			<data mode="bidirectional" />
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+		<edge from-layer="410" from-port="0" to-layer="411" to-port="1" />
+		<edge from-layer="411" from-port="2" to-layer="413" to-port="0" />
+		<edge from-layer="412" from-port="1" to-layer="413" to-port="1" />
+		<edge from-layer="413" from-port="2" to-layer="414" to-port="0" />
+	</edges>
+	<rt_info>
+		<MO_version value="2022.3.0-9052-9752fafe8eb-releases/2022/3" />
+		<Runtime_version value="2022.3.0-9052-9752fafe8eb-releases/2022/3" />
+		<conversion_parameters>
+			<framework value="onnx" />
+			<input_model value="DIR/model.onnx" />
+			<model_name value="model" />
+			<output_dir value="DIR" />
+		</conversion_parameters>
+		<legacy_frontend value="False" />
+	</rt_info>
+</net>