diff --git a/graph_net/tests/test_squeezenet1_1_extract.py b/graph_net/tests/test_squeezenet1_1_extract.py
new file mode 100644
index 000000000..2280d03e0
--- /dev/null
+++ b/graph_net/tests/test_squeezenet1_1_extract.py
@@ -0,0 +1,41 @@
+import paddle
+import os
+import json
+from paddle.vision.models import squeezenet1_1
+
+
+def extract_squeezenet():
+    # 1. Environment setup
+    os.environ["FLAGS_enable_pir_api"] = "1"
+    paddle.enable_static()
+
+    # 2. Capture computation graph
+    main_program = paddle.static.Program()
+    with paddle.static.program_guard(main_program):
+        inputs = paddle.static.data(
+            name="image", shape=[1, 3, 224, 224], dtype="float32"
+        )
+        model = squeezenet1_1(pretrained=False)
+        model(inputs)
+
+    # 3. Get absolute save path
+    current_file_path = os.path.abspath(__file__)
+    project_root = os.path.dirname(os.path.dirname(os.path.dirname(current_file_path)))
+    target_dir = os.path.join(project_root, "paddle_samples/PaddleX/squeezenet1_1")
+    save_path = os.path.join(target_dir, "graph_net.json")
+
+    # 4. Standardize format: Convert program to a valid JSON string
+    # We wrap it in a dictionary to ensure valid JSON structure with double quotes
+    program_content = str(main_program)
+    json_data = {"protocol": "PIR", "program_desc": program_content}
+
+    # 5. Write to file using json.dump (this enforces double quotes for keys)
+    os.makedirs(target_dir, exist_ok=True)
+    with open(save_path, "w") as f:
+        json.dump(json_data, f, indent=4)
+
+    print(f"Extraction successful! Standard JSON generated at: {save_path}")
+
+
+if __name__ == "__main__":
+    extract_squeezenet()
diff --git a/paddle_samples/PaddleX/squeezenet1_1/__init__.py b/paddle_samples/PaddleX/squeezenet1_1/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/paddle_samples/PaddleX/squeezenet1_1/graph_hash.txt b/paddle_samples/PaddleX/squeezenet1_1/graph_hash.txt
new file mode 100644
index 000000000..18b6e5171
--- /dev/null
+++ b/paddle_samples/PaddleX/squeezenet1_1/graph_hash.txt
@@ -0,0 +1 @@
+a87bc38868b688b562b67634fb432eab7051fff838290f80ec278d02c9391a80
\ No newline at end of file
diff --git a/paddle_samples/PaddleX/squeezenet1_1/graph_net.json b/paddle_samples/PaddleX/squeezenet1_1/graph_net.json
new file mode 100644
index 000000000..6b77276be
--- /dev/null
+++ b/paddle_samples/PaddleX/squeezenet1_1/graph_net.json
@@ -0,0 +1,4 @@
+{
+    "protocol": "PIR",
+    "program_desc": "{\n    (%0) = \"builtin.parameter\" () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:\"conv2d_25.b_0\",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> tensor<1000xf32>\n    (%1) = \"builtin.parameter\" () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:\"conv2d_25.w_0\",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> tensor<1000x512x1x1xf32>\n    (%2) = \"builtin.parameter\" () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:\"conv2d_24.b_0\",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> tensor<256xf32>\n    (%3) = \"builtin.parameter\" () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:\"conv2d_24.w_0\",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> tensor<256x64x3x3xf32>\n    (%4) = \"builtin.parameter\" () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:\"conv2d_23.b_0\",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> tensor<256xf32>\n    (%5) = \"builtin.parameter\" () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:\"conv2d_23.w_0\",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> tensor<256x64x1x1xf32>\n    (%6) = \"builtin.parameter\" () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:\"conv2d_22.b_0\",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> tensor<64xf32>\n    (%7) = \"builtin.parameter\" () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:\"conv2d_22.w_0\",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> tensor<64x512x1x1xf32>\n    (%8) = \"builtin.parameter\" () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:\"conv2d_21.b_0\",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> tensor<256xf32>\n    (%9) = \"builtin.parameter\" () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:\"conv2d_21.w_0\",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> tensor<256x64x3x3xf32>\n    (%10) = \"builtin.parameter\" () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:\"conv2d_20.b_0\",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> tensor<256xf32>\n    (%11) = \"builtin.parameter\" () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:\"conv2d_20.w_0\",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> tensor<256x64x1x1xf32>\n    (%12) = \"builtin.parameter\" () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:\"conv2d_19.b_0\",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> tensor<64xf32>\n    (%13) = \"builtin.parameter\" () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:\"conv2d_19.w_0\",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> tensor<64x384x1x1xf32>\n    (%14) = \"builtin.parameter\" () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:\"conv2d_18.b_0\",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> tensor<192xf32>\n    (%15) = \"builtin.parameter\" () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:\"conv2d_18.w_0\",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> tensor<192x48x3x3xf32>\n    (%16) = \"builtin.parameter\" () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:\"conv2d_17.b_0\",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> tensor<192xf32>\n    (%17) = \"builtin.parameter\" () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:\"conv2d_17.w_0\",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> tensor<192x48x1x1xf32>\n    (%18) = \"builtin.parameter\" () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:\"conv2d_16.b_0\",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> tensor<48xf32>\n    (%19) = \"builtin.parameter\" () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:\"conv2d_16.w_0\",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> tensor<48x384x1x1xf32>\n    (%20) = \"builtin.parameter\" () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:\"conv2d_15.b_0\",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> tensor<192xf32>\n    (%21) = \"builtin.parameter\" () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:\"conv2d_15.w_0\",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> tensor<192x48x3x3xf32>\n    (%22) = \"builtin.parameter\" () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:\"conv2d_14.b_0\",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> tensor<192xf32>\n    (%23) = \"builtin.parameter\" () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:\"conv2d_14.w_0\",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> tensor<192x48x1x1xf32>\n    (%24) = \"builtin.parameter\" () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:\"conv2d_13.b_0\",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> tensor<48xf32>\n    (%25) = \"builtin.parameter\" () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:\"conv2d_13.w_0\",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> tensor<48x256x1x1xf32>\n    (%26) = \"builtin.parameter\" () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:\"conv2d_12.b_0\",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> tensor<128xf32>\n    (%27) = \"builtin.parameter\" () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:\"conv2d_12.w_0\",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> tensor<128x32x3x3xf32>\n    (%28) = \"builtin.parameter\" () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:\"conv2d_11.b_0\",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> tensor<128xf32>\n    (%29) = \"builtin.parameter\" () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:\"conv2d_11.w_0\",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> tensor<128x32x1x1xf32>\n    (%30) = \"builtin.parameter\" () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:\"conv2d_10.b_0\",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> tensor<32xf32>\n    (%31) = \"builtin.parameter\" () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:\"conv2d_10.w_0\",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> tensor<32x256x1x1xf32>\n    (%32) = \"builtin.parameter\" () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:\"conv2d_9.b_0\",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> tensor<128xf32>\n    (%33) = \"builtin.parameter\" () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:\"conv2d_9.w_0\",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> tensor<128x32x3x3xf32>\n    (%34) = \"builtin.parameter\" () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:\"conv2d_8.b_0\",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> tensor<128xf32>\n    (%35) = \"builtin.parameter\" () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:\"conv2d_8.w_0\",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> tensor<128x32x1x1xf32>\n    (%36) = \"builtin.parameter\" () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:\"conv2d_7.b_0\",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> tensor<32xf32>\n    (%37) = \"builtin.parameter\" () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:\"conv2d_7.w_0\",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> tensor<32x128x1x1xf32>\n    (%38) = \"builtin.parameter\" () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:\"conv2d_6.b_0\",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> tensor<64xf32>\n    (%39) = \"builtin.parameter\" () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:\"conv2d_6.w_0\",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> tensor<64x16x3x3xf32>\n    (%40) = \"builtin.parameter\" () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:\"conv2d_5.b_0\",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> tensor<64xf32>\n    (%41) = \"builtin.parameter\" () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:\"conv2d_5.w_0\",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> tensor<64x16x1x1xf32>\n    (%42) = \"builtin.parameter\" () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:\"conv2d_4.b_0\",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> tensor<16xf32>\n    (%43) = \"builtin.parameter\" () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:\"conv2d_4.w_0\",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> tensor<16x128x1x1xf32>\n    (%44) = \"builtin.parameter\" () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:\"conv2d_3.b_0\",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> tensor<64xf32>\n    (%45) = \"builtin.parameter\" () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:\"conv2d_3.w_0\",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> tensor<64x16x3x3xf32>\n    (%46) = \"builtin.parameter\" () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:\"conv2d_2.b_0\",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> tensor<64xf32>\n    (%47) = \"builtin.parameter\" () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:\"conv2d_2.w_0\",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> tensor<64x16x1x1xf32>\n    (%48) = \"builtin.parameter\" () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:\"conv2d_1.b_0\",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> tensor<16xf32>\n    (%49) = \"builtin.parameter\" () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:\"conv2d_1.w_0\",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> tensor<16x64x1x1xf32>\n    (%50) = \"builtin.parameter\" () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:\"conv2d_0.b_0\",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> tensor<64xf32>\n    (%51) = \"builtin.parameter\" () {is_distributed:[false],is_parameter:[true],need_clip:[true],parameter_name:\"conv2d_0.w_0\",persistable:[true],stop_gradient:[false],trainable:[true]} : () -> tensor<64x3x3x3xf32>\n    (%52) = \"pd_op.data\" () {dtype:float32,name:\"image\",place:Place(undefined:0),shape:[1,3,224,224],stop_gradient:[true]} : () -> tensor<1x3x224x224xf32>\n    (%53) = \"pd_op.conv2d\" (%52, %51) {data_format:\"NCHW\",dilations:[1,1],groups:1,padding_algorithm:\"EXPLICIT\",paddings:[1,1],stop_gradient:[false],strides:[2,2],struct_name:\"/SqueezeNet/Conv2D/\"} : (tensor<1x3x224x224xf32>, tensor<64x3x3x3xf32>) -> tensor<1x64x112x112xf32>\n    (%54) = \"pd_op.full_int_array\" () {dtype:int64,place:Place(cpu),stop_gradient:[true],struct_name:\"/SqueezeNet/Conv2D/\",value:[1,-1,1,1]} : () -> tensor<4xi64>\n    (%55) = \"pd_op.reshape\" (%50, %54) {stop_gradient:[false],struct_name:\"/SqueezeNet/Conv2D/\"} : (tensor<64xf32>, tensor<4xi64>) -> tensor<1x64x1x1xf32>\n    (%56) = \"pd_op.add\" (%53, %55) {stop_gradient:[false],struct_name:\"/SqueezeNet/Conv2D/\"} : (tensor<1x64x112x112xf32>, tensor<1x64x1x1xf32>) -> tensor<1x64x112x112xf32>\n    (%57) = \"pd_op.relu\" (%56) {stop_gradient:[false],struct_name:\"/SqueezeNet/\"} : (tensor<1x64x112x112xf32>) -> tensor<1x64x112x112xf32>\n    (%58) = \"pd_op.full_int_array\" () {dtype:int64,place:Place(cpu),stop_gradient:[true],struct_name:\"/SqueezeNet/MaxPool2D/\",value:[3,3]} : () -> tensor<2xi64>\n    (%59) = \"pd_op.pool2d\" (%57, %58) {adaptive:false,ceil_mode:false,data_format:\"NCHW\",exclusive:true,global_pooling:false,padding_algorithm:\"EXPLICIT\",paddings:[0,0],pooling_type:\"max\",stop_gradient:[false],strides:[2,2],struct_name:\"/SqueezeNet/MaxPool2D/\"} : (tensor<1x64x112x112xf32>, tensor<2xi64>) -> tensor<1x64x55x55xf32>\n    (%60) = \"pd_op.conv2d\" (%59, %49) {data_format:\"NCHW\",dilations:[1,1],groups:1,padding_algorithm:\"EXPLICIT\",paddings:[0,0],stop_gradient:[false],strides:[1,1],struct_name:\"/SqueezeNet/MakeFire/MakeFireConv/Conv2D/\"} : (tensor<1x64x55x55xf32>, tensor<16x64x1x1xf32>) -> tensor<1x16x55x55xf32>\n    (%61) = \"pd_op.full_int_array\" () {dtype:int64,place:Place(cpu),stop_gradient:[true],struct_name:\"/SqueezeNet/MakeFire/MakeFireConv/Conv2D/\",value:[1,-1,1,1]} : () -> tensor<4xi64>\n    (%62) = \"pd_op.reshape\" (%48, %61) {stop_gradient:[false],struct_name:\"/SqueezeNet/MakeFire/MakeFireConv/Conv2D/\"} : (tensor<16xf32>, tensor<4xi64>) -> tensor<1x16x1x1xf32>\n    (%63) = \"pd_op.add\" (%60, %62) {stop_gradient:[false],struct_name:\"/SqueezeNet/MakeFire/MakeFireConv/Conv2D/\"} : (tensor<1x16x55x55xf32>, tensor<1x16x1x1xf32>) -> tensor<1x16x55x55xf32>\n    (%64) = \"pd_op.relu\" (%63) {stop_gradient:[false],struct_name:\"/SqueezeNet/MakeFire/MakeFireConv/\"} : (tensor<1x16x55x55xf32>) -> tensor<1x16x55x55xf32>\n    (%65) = \"pd_op.conv2d\" (%64, %47) {data_format:\"NCHW\",dilations:[1,1],groups:1,padding_algorithm:\"EXPLICIT\",paddings:[0,0],stop_gradient:[false],strides:[1,1],struct_name:\"/SqueezeNet/MakeFire/MakeFireConv_1/Conv2D/\"} : (tensor<1x16x55x55xf32>, tensor<64x16x1x1xf32>) -> tensor<1x64x55x55xf32>\n    (%66) = \"pd_op.full_int_array\" () {dtype:int64,place:Place(cpu),stop_gradient:[true],struct_name:\"/SqueezeNet/MakeFire/MakeFireConv_1/Conv2D/\",value:[1,-1,1,1]} : () -> tensor<4xi64>\n    (%67) = \"pd_op.reshape\" (%46, %66) {stop_gradient:[false],struct_name:\"/SqueezeNet/MakeFire/MakeFireConv_1/Conv2D/\"} : (tensor<64xf32>, tensor<4xi64>) -> tensor<1x64x1x1xf32>\n    (%68) = \"pd_op.add\" (%65, %67) {stop_gradient:[false],struct_name:\"/SqueezeNet/MakeFire/MakeFireConv_1/Conv2D/\"} : (tensor<1x64x55x55xf32>, tensor<1x64x1x1xf32>) -> tensor<1x64x55x55xf32>\n    (%69) = \"pd_op.relu\" (%68) {stop_gradient:[false],struct_name:\"/SqueezeNet/MakeFire/MakeFireConv_1/\"} : (tensor<1x64x55x55xf32>) -> tensor<1x64x55x55xf32>\n    (%70) = \"pd_op.conv2d\" (%64, %45) {data_format:\"NCHW\",dilations:[1,1],groups:1,padding_algorithm:\"EXPLICIT\",paddings:[1,1],stop_gradient:[false],strides:[1,1],struct_name:\"/SqueezeNet/MakeFire/MakeFireConv_2/Conv2D/\"} : (tensor<1x16x55x55xf32>, tensor<64x16x3x3xf32>) -> tensor<1x64x55x55xf32>\n    (%71) = \"pd_op.full_int_array\" () {dtype:int64,place:Place(cpu),stop_gradient:[true],struct_name:\"/SqueezeNet/MakeFire/MakeFireConv_2/Conv2D/\",value:[1,-1,1,1]} : () -> tensor<4xi64>\n    (%72) = \"pd_op.reshape\" (%44, %71) {stop_gradient:[false],struct_name:\"/SqueezeNet/MakeFire/MakeFireConv_2/Conv2D/\"} : (tensor<64xf32>, tensor<4xi64>) -> tensor<1x64x1x1xf32>\n    (%73) = \"pd_op.add\" (%70, %72) {stop_gradient:[false],struct_name:\"/SqueezeNet/MakeFire/MakeFireConv_2/Conv2D/\"} : (tensor<1x64x55x55xf32>, tensor<1x64x1x1xf32>) -> tensor<1x64x55x55xf32>\n    (%74) = \"pd_op.relu\" (%73) {stop_gradient:[false],struct_name:\"/SqueezeNet/MakeFire/MakeFireConv_2/\"} : (tensor<1x64x55x55xf32>) -> tensor<1x64x55x55xf32>\n    (%75) = \"pd_op.full\" () {dtype:int32,place:Place(cpu),shape:[1],stop_gradient:[true],struct_name:\"/SqueezeNet/MakeFire/\",value:1} : () -> tensor<1xi32>\n    (%76) = \"builtin.combine\" (%69, %74) {stop_gradient:[false],struct_name:\"/SqueezeNet/MakeFire/\"} : (tensor<1x64x55x55xf32>, tensor<1x64x55x55xf32>) -> vec[tensor<1x64x55x55xf32>,tensor<1x64x55x55xf32>]\n    (%77) = \"pd_op.concat\" (%76, %75) {stop_gradient:[false],struct_name:\"/SqueezeNet/MakeFire/\"} : (vec[tensor<1x64x55x55xf32>,tensor<1x64x55x55xf32>], tensor<1xi32>) -> tensor<1x128x55x55xf32>\n    (%78) = \"pd_op.conv2d\" (%77, %43) {data_format:\"NCHW\",dilations:[1,1],groups:1,padding_algorithm:\"EXPLICIT\",paddings:[0,0],stop_gradient:[false],strides:[1,1],struct_name:\"/SqueezeNet/MakeFire_1/MakeFireConv/Conv2D/\"} : (tensor<1x128x55x55xf32>, tensor<16x128x1x1xf32>) -> tensor<1x16x55x55xf32>\n    (%79) = \"pd_op.full_int_array\" () {dtype:int64,place:Place(cpu),stop_gradient:[true],struct_name:\"/SqueezeNet/MakeFire_1/MakeFireConv/Conv2D/\",value:[1,-1,1,1]} : () -> tensor<4xi64>\n    (%80) = \"pd_op.reshape\" (%42, %79) {stop_gradient:[false],struct_name:\"/SqueezeNet/MakeFire_1/MakeFireConv/Conv2D/\"} : (tensor<16xf32>, tensor<4xi64>) -> tensor<1x16x1x1xf32>\n    (%81) = \"pd_op.add\" (%78, %80) {stop_gradient:[false],struct_name:\"/SqueezeNet/MakeFire_1/MakeFireConv/Conv2D/\"} : (tensor<1x16x55x55xf32>, tensor<1x16x1x1xf32>) -> tensor<1x16x55x55xf32>\n    (%82) = \"pd_op.relu\" (%81) {stop_gradient:[false],struct_name:\"/SqueezeNet/MakeFire_1/MakeFireConv/\"} : (tensor<1x16x55x55xf32>) -> tensor<1x16x55x55xf32>\n    (%83) = \"pd_op.conv2d\" (%82, %41) {data_format:\"NCHW\",dilations:[1,1],groups:1,padding_algorithm:\"EXPLICIT\",paddings:[0,0],stop_gradient:[false],strides:[1,1],struct_name:\"/SqueezeNet/MakeFire_1/MakeFireConv_1/Conv2D/\"} : (tensor<1x16x55x55xf32>, tensor<64x16x1x1xf32>) -> tensor<1x64x55x55xf32>\n    (%84) = \"pd_op.full_int_array\" () {dtype:int64,place:Place(cpu),stop_gradient:[true],struct_name:\"/SqueezeNet/MakeFire_1/MakeFireConv_1/Conv2D/\",value:[1,-1,1,1]} : () -> tensor<4xi64>\n    (%85) = \"pd_op.reshape\" (%40, %84) {stop_gradient:[false],struct_name:\"/SqueezeNet/MakeFire_1/MakeFireConv_1/Conv2D/\"} : (tensor<64xf32>, tensor<4xi64>) -> tensor<1x64x1x1xf32>\n    (%86) = \"pd_op.add\" (%83, %85) {stop_gradient:[false],struct_name:\"/SqueezeNet/MakeFire_1/MakeFireConv_1/Conv2D/\"} : (tensor<1x64x55x55xf32>, tensor<1x64x1x1xf32>) -> tensor<1x64x55x55xf32>\n    (%87) = \"pd_op.relu\" (%86) {stop_gradient:[false],struct_name:\"/SqueezeNet/MakeFire_1/MakeFireConv_1/\"} : (tensor<1x64x55x55xf32>) -> tensor<1x64x55x55xf32>\n    (%88) = \"pd_op.conv2d\" (%82, %39) {data_format:\"NCHW\",dilations:[1,1],groups:1,padding_algorithm:\"EXPLICIT\",paddings:[1,1],stop_gradient:[false],strides:[1,1],struct_name:\"/SqueezeNet/MakeFire_1/MakeFireConv_2/Conv2D/\"} : (tensor<1x16x55x55xf32>, tensor<64x16x3x3xf32>) -> tensor<1x64x55x55xf32>\n    (%89) = \"pd_op.full_int_array\" () {dtype:int64,place:Place(cpu),stop_gradient:[true],struct_name:\"/SqueezeNet/MakeFire_1/MakeFireConv_2/Conv2D/\",value:[1,-1,1,1]} : () -> tensor<4xi64>\n    (%90) = \"pd_op.reshape\" (%38, %89) {stop_gradient:[false],struct_name:\"/SqueezeNet/MakeFire_1/MakeFireConv_2/Conv2D/\"} : (tensor<64xf32>, tensor<4xi64>) -> tensor<1x64x1x1xf32>\n    (%91) = \"pd_op.add\" (%88, %90) {stop_gradient:[false],struct_name:\"/SqueezeNet/MakeFire_1/MakeFireConv_2/Conv2D/\"} : (tensor<1x64x55x55xf32>, tensor<1x64x1x1xf32>) -> tensor<1x64x55x55xf32>\n    (%92) = \"pd_op.relu\" (%91) {stop_gradient:[false],struct_name:\"/SqueezeNet/MakeFire_1/MakeFireConv_2/\"} : (tensor<1x64x55x55xf32>) -> tensor<1x64x55x55xf32>\n    (%93) = \"pd_op.full\" () {dtype:int32,place:Place(cpu),shape:[1],stop_gradient:[true],struct_name:\"/SqueezeNet/MakeFire_1/\",value:1} : () -> tensor<1xi32>\n    (%94) = \"builtin.combine\" (%87, %92) {stop_gradient:[false],struct_name:\"/SqueezeNet/MakeFire_1/\"} : (tensor<1x64x55x55xf32>, tensor<1x64x55x55xf32>) -> vec[tensor<1x64x55x55xf32>,tensor<1x64x55x55xf32>]\n    (%95) = \"pd_op.concat\" (%94, %93) {stop_gradient:[false],struct_name:\"/SqueezeNet/MakeFire_1/\"} : (vec[tensor<1x64x55x55xf32>,tensor<1x64x55x55xf32>], tensor<1xi32>) -> tensor<1x128x55x55xf32>\n    (%96) = \"pd_op.full_int_array\" () {dtype:int64,place:Place(cpu),stop_gradient:[true],struct_name:\"/SqueezeNet/MaxPool2D_1/\",value:[3,3]} : () -> tensor<2xi64>\n    (%97) = \"pd_op.pool2d\" (%95, %96) {adaptive:false,ceil_mode:false,data_format:\"NCHW\",exclusive:true,global_pooling:false,padding_algorithm:\"EXPLICIT\",paddings:[0,0],pooling_type:\"max\",stop_gradient:[false],strides:[2,2],struct_name:\"/SqueezeNet/MaxPool2D_1/\"} : (tensor<1x128x55x55xf32>, tensor<2xi64>) -> tensor<1x128x27x27xf32>\n    (%98) = \"pd_op.conv2d\" (%97, %37) {data_format:\"NCHW\",dilations:[1,1],groups:1,padding_algorithm:\"EXPLICIT\",paddings:[0,0],stop_gradient:[false],strides:[1,1],struct_name:\"/SqueezeNet/MakeFire_2/MakeFireConv/Conv2D/\"} : (tensor<1x128x27x27xf32>, tensor<32x128x1x1xf32>) -> tensor<1x32x27x27xf32>\n    (%99) = \"pd_op.full_int_array\" () {dtype:int64,place:Place(cpu),stop_gradient:[true],struct_name:\"/SqueezeNet/MakeFire_2/MakeFireConv/Conv2D/\",value:[1,-1,1,1]} : () -> tensor<4xi64>\n    (%100) = \"pd_op.reshape\" (%36, %99) {stop_gradient:[false],struct_name:\"/SqueezeNet/MakeFire_2/MakeFireConv/Conv2D/\"} : (tensor<32xf32>, tensor<4xi64>) -> tensor<1x32x1x1xf32>\n    (%101) = \"pd_op.add\" (%98, %100) {stop_gradient:[false],struct_name:\"/SqueezeNet/MakeFire_2/MakeFireConv/Conv2D/\"} : (tensor<1x32x27x27xf32>, tensor<1x32x1x1xf32>) -> tensor<1x32x27x27xf32>\n    (%102) = \"pd_op.relu\" (%101) {stop_gradient:[false],struct_name:\"/SqueezeNet/MakeFire_2/MakeFireConv/\"} : (tensor<1x32x27x27xf32>) -> tensor<1x32x27x27xf32>\n    (%103) = \"pd_op.conv2d\" (%102, %35) {data_format:\"NCHW\",dilations:[1,1],groups:1,padding_algorithm:\"EXPLICIT\",paddings:[0,0],stop_gradient:[false],strides:[1,1],struct_name:\"/SqueezeNet/MakeFire_2/MakeFireConv_1/Conv2D/\"} : (tensor<1x32x27x27xf32>, tensor<128x32x1x1xf32>) -> tensor<1x128x27x27xf32>\n    (%104) = \"pd_op.full_int_array\" () {dtype:int64,place:Place(cpu),stop_gradient:[true],struct_name:\"/SqueezeNet/MakeFire_2/MakeFireConv_1/Conv2D/\",value:[1,-1,1,1]} : () -> tensor<4xi64>\n    (%105) = \"pd_op.reshape\" (%34, %104) {stop_gradient:[false],struct_name:\"/SqueezeNet/MakeFire_2/MakeFireConv_1/Conv2D/\"} : (tensor<128xf32>, tensor<4xi64>) -> tensor<1x128x1x1xf32>\n    (%106) = \"pd_op.add\" (%103, %105) {stop_gradient:[false],struct_name:\"/SqueezeNet/MakeFire_2/MakeFireConv_1/Conv2D/\"} : (tensor<1x128x27x27xf32>, tensor<1x128x1x1xf32>) -> tensor<1x128x27x27xf32>\n    (%107) = \"pd_op.relu\" (%106) {stop_gradient:[false],struct_name:\"/SqueezeNet/MakeFire_2/MakeFireConv_1/\"} : (tensor<1x128x27x27xf32>) -> tensor<1x128x27x27xf32>\n    (%108) = \"pd_op.conv2d\" (%102, %33) {data_format:\"NCHW\",dilations:[1,1],groups:1,padding_algorithm:\"EXPLICIT\",paddings:[1,1],stop_gradient:[false],strides:[1,1],struct_name:\"/SqueezeNet/MakeFire_2/MakeFireConv_2/Conv2D/\"} : (tensor<1x32x27x27xf32>, tensor<128x32x3x3xf32>) -> tensor<1x128x27x27xf32>\n    (%109) = \"pd_op.full_int_array\" () {dtype:int64,place:Place(cpu),stop_gradient:[true],struct_name:\"/SqueezeNet/MakeFire_2/MakeFireConv_2/Conv2D/\",value:[1,-1,1,1]} : () -> tensor<4xi64>\n    (%110) = \"pd_op.reshape\" (%32, %109) {stop_gradient:[false],struct_name:\"/SqueezeNet/MakeFire_2/MakeFireConv_2/Conv2D/\"} : (tensor<128xf32>, tensor<4xi64>) -> tensor<1x128x1x1xf32>\n    (%111) = \"pd_op.add\" (%108, %110) {stop_gradient:[false],struct_name:\"/SqueezeNet/MakeFire_2/MakeFireConv_2/Conv2D/\"} : (tensor<1x128x27x27xf32>, tensor<1x128x1x1xf32>) -> tensor<1x128x27x27xf32>\n    (%112) = \"pd_op.relu\" (%111) {stop_gradient:[false],struct_name:\"/SqueezeNet/MakeFire_2/MakeFireConv_2/\"} : (tensor<1x128x27x27xf32>) -> tensor<1x128x27x27xf32>\n    (%113) = \"pd_op.full\" () {dtype:int32,place:Place(cpu),shape:[1],stop_gradient:[true],struct_name:\"/SqueezeNet/MakeFire_2/\",value:1} : () -> tensor<1xi32>\n    (%114) = \"builtin.combine\" (%107, %112) {stop_gradient:[false],struct_name:\"/SqueezeNet/MakeFire_2/\"} : (tensor<1x128x27x27xf32>, tensor<1x128x27x27xf32>) -> vec[tensor<1x128x27x27xf32>,tensor<1x128x27x27xf32>]\n    (%115) = \"pd_op.concat\" (%114, %113) {stop_gradient:[false],struct_name:\"/SqueezeNet/MakeFire_2/\"} : (vec[tensor<1x128x27x27xf32>,tensor<1x128x27x27xf32>], tensor<1xi32>) -> tensor<1x256x27x27xf32>\n    (%116) = \"pd_op.conv2d\" (%115, %31) {data_format:\"NCHW\",dilations:[1,1],groups:1,padding_algorithm:\"EXPLICIT\",paddings:[0,0],stop_gradient:[false],strides:[1,1],struct_name:\"/SqueezeNet/MakeFire_3/MakeFireConv/Conv2D/\"} : (tensor<1x256x27x27xf32>, tensor<32x256x1x1xf32>) -> tensor<1x32x27x27xf32>\n    (%117) = \"pd_op.full_int_array\" () {dtype:int64,place:Place(cpu),stop_gradient:[true],struct_name:\"/SqueezeNet/MakeFire_3/MakeFireConv/Conv2D/\",value:[1,-1,1,1]} : () -> tensor<4xi64>\n    (%118) = \"pd_op.reshape\" (%30, %117) {stop_gradient:[false],struct_name:\"/SqueezeNet/MakeFire_3/MakeFireConv/Conv2D/\"} : (tensor<32xf32>, tensor<4xi64>) -> tensor<1x32x1x1xf32>\n    (%119) = \"pd_op.add\" (%116, %118) {stop_gradient:[false],struct_name:\"/SqueezeNet/MakeFire_3/MakeFireConv/Conv2D/\"} : (tensor<1x32x27x27xf32>, tensor<1x32x1x1xf32>) -> tensor<1x32x27x27xf32>\n    (%120) = \"pd_op.relu\" (%119) {stop_gradient:[false],struct_name:\"/SqueezeNet/MakeFire_3/MakeFireConv/\"} : (tensor<1x32x27x27xf32>) -> tensor<1x32x27x27xf32>\n    (%121) = \"pd_op.conv2d\" (%120, %29) {data_format:\"NCHW\",dilations:[1,1],groups:1,padding_algorithm:\"EXPLICIT\",paddings:[0,0],stop_gradient:[false],strides:[1,1],struct_name:\"/SqueezeNet/MakeFire_3/MakeFireConv_1/Conv2D/\"} : (tensor<1x32x27x27xf32>, tensor<128x32x1x1xf32>) -> tensor<1x128x27x27xf32>\n    (%122) = \"pd_op.full_int_array\" () {dtype:int64,place:Place(cpu),stop_gradient:[true],struct_name:\"/SqueezeNet/MakeFire_3/MakeFireConv_1/Conv2D/\",value:[1,-1,1,1]} : () -> tensor<4xi64>\n    (%123) = \"pd_op.reshape\" (%28, %122) {stop_gradient:[false],struct_name:\"/SqueezeNet/MakeFire_3/MakeFireConv_1/Conv2D/\"} : (tensor<128xf32>, tensor<4xi64>) -> tensor<1x128x1x1xf32>\n    (%124) = \"pd_op.add\" (%121, %123) {stop_gradient:[false],struct_name:\"/SqueezeNet/MakeFire_3/MakeFireConv_1/Conv2D/\"} : (tensor<1x128x27x27xf32>, tensor<1x128x1x1xf32>) -> tensor<1x128x27x27xf32>\n    (%125) = \"pd_op.relu\" (%124) {stop_gradient:[false],struct_name:\"/SqueezeNet/MakeFire_3/MakeFireConv_1/\"} : (tensor<1x128x27x27xf32>) -> tensor<1x128x27x27xf32>\n    (%126) = \"pd_op.conv2d\" (%120, %27) {data_format:\"NCHW\",dilations:[1,1],groups:1,padding_algorithm:\"EXPLICIT\",paddings:[1,1],stop_gradient:[false],strides:[1,1],struct_name:\"/SqueezeNet/MakeFire_3/MakeFireConv_2/Conv2D/\"} : (tensor<1x32x27x27xf32>, tensor<128x32x3x3xf32>) -> tensor<1x128x27x27xf32>\n    (%127) = \"pd_op.full_int_array\" () {dtype:int64,place:Place(cpu),stop_gradient:[true],struct_name:\"/SqueezeNet/MakeFire_3/MakeFireConv_2/Conv2D/\",value:[1,-1,1,1]} : () -> tensor<4xi64>\n    (%128) = \"pd_op.reshape\" (%26, %127) {stop_gradient:[false],struct_name:\"/SqueezeNet/MakeFire_3/MakeFireConv_2/Conv2D/\"} : (tensor<128xf32>, tensor<4xi64>) -> tensor<1x128x1x1xf32>\n    (%129) = \"pd_op.add\" (%126, %128) {stop_gradient:[false],struct_name:\"/SqueezeNet/MakeFire_3/MakeFireConv_2/Conv2D/\"} : (tensor<1x128x27x27xf32>, tensor<1x128x1x1xf32>) -> tensor<1x128x27x27xf32>\n    (%130) = \"pd_op.relu\" (%129) {stop_gradient:[false],struct_name:\"/SqueezeNet/MakeFire_3/MakeFireConv_2/\"} : (tensor<1x128x27x27xf32>) -> tensor<1x128x27x27xf32>\n    (%131) = \"pd_op.full\" () {dtype:int32,place:Place(cpu),shape:[1],stop_gradient:[true],struct_name:\"/SqueezeNet/MakeFire_3/\",value:1} : () -> tensor<1xi32>\n    (%132) = \"builtin.combine\" (%125, %130) {stop_gradient:[false],struct_name:\"/SqueezeNet/MakeFire_3/\"} : (tensor<1x128x27x27xf32>, tensor<1x128x27x27xf32>) -> vec[tensor<1x128x27x27xf32>,tensor<1x128x27x27xf32>]\n    (%133) = \"pd_op.concat\" (%132, %131) {stop_gradient:[false],struct_name:\"/SqueezeNet/MakeFire_3/\"} : (vec[tensor<1x128x27x27xf32>,tensor<1x128x27x27xf32>], tensor<1xi32>) -> tensor<1x256x27x27xf32>\n    (%134) = \"pd_op.full_int_array\" () {dtype:int64,place:Place(cpu),stop_gradient:[true],struct_name:\"/SqueezeNet/MaxPool2D_2/\",value:[3,3]} : () -> tensor<2xi64>\n    (%135) = \"pd_op.pool2d\" (%133, %134) {adaptive:false,ceil_mode:false,data_format:\"NCHW\",exclusive:true,global_pooling:false,padding_algorithm:\"EXPLICIT\",paddings:[0,0],pooling_type:\"max\",stop_gradient:[false],strides:[2,2],struct_name:\"/SqueezeNet/MaxPool2D_2/\"} : (tensor<1x256x27x27xf32>, tensor<2xi64>) -> tensor<1x256x13x13xf32>\n    (%136) = \"pd_op.conv2d\" (%135, %25) {data_format:\"NCHW\",dilations:[1,1],groups:1,padding_algorithm:\"EXPLICIT\",paddings:[0,0],stop_gradient:[false],strides:[1,1],struct_name:\"/SqueezeNet/MakeFire_4/MakeFireConv/Conv2D/\"} : (tensor<1x256x13x13xf32>, tensor<48x256x1x1xf32>) -> tensor<1x48x13x13xf32>\n    (%137) = \"pd_op.full_int_array\" () {dtype:int64,place:Place(cpu),stop_gradient:[true],struct_name:\"/SqueezeNet/MakeFire_4/MakeFireConv/Conv2D/\",value:[1,-1,1,1]} : () -> tensor<4xi64>\n    (%138) = \"pd_op.reshape\" (%24, %137) {stop_gradient:[false],struct_name:\"/SqueezeNet/MakeFire_4/MakeFireConv/Conv2D/\"} : (tensor<48xf32>, tensor<4xi64>) -> tensor<1x48x1x1xf32>\n    (%139) = \"pd_op.add\" (%136, %138) {stop_gradient:[false],struct_name:\"/SqueezeNet/MakeFire_4/MakeFireConv/Conv2D/\"} : (tensor<1x48x13x13xf32>, tensor<1x48x1x1xf32>) -> tensor<1x48x13x13xf32>\n    (%140) = \"pd_op.relu\" (%139) {stop_gradient:[false],struct_name:\"/SqueezeNet/MakeFire_4/MakeFireConv/\"} : (tensor<1x48x13x13xf32>) -> tensor<1x48x13x13xf32>\n    (%141) = \"pd_op.conv2d\" (%140, %23) {data_format:\"NCHW\",dilations:[1,1],groups:1,padding_algorithm:\"EXPLICIT\",paddings:[0,0],stop_gradient:[false],strides:[1,1],struct_name:\"/SqueezeNet/MakeFire_4/MakeFireConv_1/Conv2D/\"} : (tensor<1x48x13x13xf32>, tensor<192x48x1x1xf32>) -> tensor<1x192x13x13xf32>\n    (%142) = \"pd_op.full_int_array\" () {dtype:int64,place:Place(cpu),stop_gradient:[true],struct_name:\"/SqueezeNet/MakeFire_4/MakeFireConv_1/Conv2D/\",value:[1,-1,1,1]} : () -> tensor<4xi64>\n    (%143) = \"pd_op.reshape\" (%22, %142) {stop_gradient:[false],struct_name:\"/SqueezeNet/MakeFire_4/MakeFireConv_1/Conv2D/\"} : (tensor<192xf32>, tensor<4xi64>) -> tensor<1x192x1x1xf32>\n    (%144) = \"pd_op.add\" (%141, %143) {stop_gradient:[false],struct_name:\"/SqueezeNet/MakeFire_4/MakeFireConv_1/Conv2D/\"} : (tensor<1x192x13x13xf32>, tensor<1x192x1x1xf32>) -> tensor<1x192x13x13xf32>\n    (%145) = \"pd_op.relu\" (%144) {stop_gradient:[false],struct_name:\"/SqueezeNet/MakeFire_4/MakeFireConv_1/\"} : (tensor<1x192x13x13xf32>) -> tensor<1x192x13x13xf32>\n    (%146) = \"pd_op.conv2d\" (%140, %21) {data_format:\"NCHW\",dilations:[1,1],groups:1,padding_algorithm:\"EXPLICIT\",paddings:[1,1],stop_gradient:[false],strides:[1,1],struct_name:\"/SqueezeNet/MakeFire_4/MakeFireConv_2/Conv2D/\"} : (tensor<1x48x13x13xf32>, tensor<192x48x3x3xf32>) -> tensor<1x192x13x13xf32>\n    (%147) = \"pd_op.full_int_array\" () {dtype:int64,place:Place(cpu),stop_gradient:[true],struct_name:\"/SqueezeNet/MakeFire_4/MakeFireConv_2/Conv2D/\",value:[1,-1,1,1]} : () -> tensor<4xi64>\n    (%148) = \"pd_op.reshape\" (%20, %147) {stop_gradient:[false],struct_name:\"/SqueezeNet/MakeFire_4/MakeFireConv_2/Conv2D/\"} : (tensor<192xf32>, tensor<4xi64>) -> tensor<1x192x1x1xf32>\n    (%149) = \"pd_op.add\" (%146, %148) {stop_gradient:[false],struct_name:\"/SqueezeNet/MakeFire_4/MakeFireConv_2/Conv2D/\"} : (tensor<1x192x13x13xf32>, tensor<1x192x1x1xf32>) -> tensor<1x192x13x13xf32>\n    (%150) = \"pd_op.relu\" (%149) {stop_gradient:[false],struct_name:\"/SqueezeNet/MakeFire_4/MakeFireConv_2/\"} : (tensor<1x192x13x13xf32>) -> tensor<1x192x13x13xf32>\n    (%151) = \"pd_op.full\" () {dtype:int32,place:Place(cpu),shape:[1],stop_gradient:[true],struct_name:\"/SqueezeNet/MakeFire_4/\",value:1} : () -> tensor<1xi32>\n    (%152) = \"builtin.combine\" (%145, %150) {stop_gradient:[false],struct_name:\"/SqueezeNet/MakeFire_4/\"} : (tensor<1x192x13x13xf32>, tensor<1x192x13x13xf32>) -> vec[tensor<1x192x13x13xf32>,tensor<1x192x13x13xf32>]\n    (%153) = \"pd_op.concat\" (%152, %151) {stop_gradient:[false],struct_name:\"/SqueezeNet/MakeFire_4/\"} : (vec[tensor<1x192x13x13xf32>,tensor<1x192x13x13xf32>], tensor<1xi32>) -> tensor<1x384x13x13xf32>\n    (%154) = \"pd_op.conv2d\" (%153, %19) {data_format:\"NCHW\",dilations:[1,1],groups:1,padding_algorithm:\"EXPLICIT\",paddings:[0,0],stop_gradient:[false],strides:[1,1],struct_name:\"/SqueezeNet/MakeFire_5/MakeFireConv/Conv2D/\"} : (tensor<1x384x13x13xf32>, tensor<48x384x1x1xf32>) -> tensor<1x48x13x13xf32>\n    (%155) = \"pd_op.full_int_array\" () {dtype:int64,place:Place(cpu),stop_gradient:[true],struct_name:\"/SqueezeNet/MakeFire_5/MakeFireConv/Conv2D/\",value:[1,-1,1,1]} : () -> tensor<4xi64>\n    (%156) = \"pd_op.reshape\" (%18, %155) {stop_gradient:[false],struct_name:\"/SqueezeNet/MakeFire_5/MakeFireConv/Conv2D/\"} : (tensor<48xf32>, tensor<4xi64>) -> tensor<1x48x1x1xf32>\n    (%157) = \"pd_op.add\" (%154, %156) {stop_gradient:[false],struct_name:\"/SqueezeNet/MakeFire_5/MakeFireConv/Conv2D/\"} : (tensor<1x48x13x13xf32>, tensor<1x48x1x1xf32>) -> tensor<1x48x13x13xf32>\n    (%158) = \"pd_op.relu\" (%157) {stop_gradient:[false],struct_name:\"/SqueezeNet/MakeFire_5/MakeFireConv/\"} : (tensor<1x48x13x13xf32>) -> tensor<1x48x13x13xf32>\n    (%159) = \"pd_op.conv2d\" (%158, %17) {data_format:\"NCHW\",dilations:[1,1],groups:1,padding_algorithm:\"EXPLICIT\",paddings:[0,0],stop_gradient:[false],strides:[1,1],struct_name:\"/SqueezeNet/MakeFire_5/MakeFireConv_1/Conv2D/\"} : (tensor<1x48x13x13xf32>, tensor<192x48x1x1xf32>) -> tensor<1x192x13x13xf32>\n    (%160) = \"pd_op.full_int_array\" () {dtype:int64,place:Place(cpu),stop_gradient:[true],struct_name:\"/SqueezeNet/MakeFire_5/MakeFireConv_1/Conv2D/\",value:[1,-1,1,1]} : () -> tensor<4xi64>\n    (%161) = \"pd_op.reshape\" (%16, %160) {stop_gradient:[false],struct_name:\"/SqueezeNet/MakeFire_5/MakeFireConv_1/Conv2D/\"} : (tensor<192xf32>, tensor<4xi64>) -> tensor<1x192x1x1xf32>\n    (%162) = \"pd_op.add\" (%159, %161) {stop_gradient:[false],struct_name:\"/SqueezeNet/MakeFire_5/MakeFireConv_1/Conv2D/\"} : (tensor<1x192x13x13xf32>, tensor<1x192x1x1xf32>) -> tensor<1x192x13x13xf32>\n    (%163) = \"pd_op.relu\" (%162) {stop_gradient:[false],struct_name:\"/SqueezeNet/MakeFire_5/MakeFireConv_1/\"} : (tensor<1x192x13x13xf32>) -> tensor<1x192x13x13xf32>\n    (%164) = \"pd_op.conv2d\" (%158, %15) {data_format:\"NCHW\",dilations:[1,1],groups:1,padding_algorithm:\"EXPLICIT\",paddings:[1,1],stop_gradient:[false],strides:[1,1],struct_name:\"/SqueezeNet/MakeFire_5/MakeFireConv_2/Conv2D/\"} : (tensor<1x48x13x13xf32>, tensor<192x48x3x3xf32>) -> tensor<1x192x13x13xf32>\n    (%165) = \"pd_op.full_int_array\" () {dtype:int64,place:Place(cpu),stop_gradient:[true],struct_name:\"/SqueezeNet/MakeFire_5/MakeFireConv_2/Conv2D/\",value:[1,-1,1,1]} : () -> tensor<4xi64>\n    (%166) = \"pd_op.reshape\" (%14, %165) {stop_gradient:[false],struct_name:\"/SqueezeNet/MakeFire_5/MakeFireConv_2/Conv2D/\"} : (tensor<192xf32>, tensor<4xi64>) -> tensor<1x192x1x1xf32>\n    (%167) = \"pd_op.add\" (%164, %166) {stop_gradient:[false],struct_name:\"/SqueezeNet/MakeFire_5/MakeFireConv_2/Conv2D/\"} : (tensor<1x192x13x13xf32>, tensor<1x192x1x1xf32>) -> tensor<1x192x13x13xf32>\n    (%168) = \"pd_op.relu\" (%167) {stop_gradient:[false],struct_name:\"/SqueezeNet/MakeFire_5/MakeFireConv_2/\"} : (tensor<1x192x13x13xf32>) -> tensor<1x192x13x13xf32>\n    (%169) = \"pd_op.full\" () {dtype:int32,place:Place(cpu),shape:[1],stop_gradient:[true],struct_name:\"/SqueezeNet/MakeFire_5/\",value:1} : () -> tensor<1xi32>\n    (%170) = \"builtin.combine\" (%163, %168) {stop_gradient:[false],struct_name:\"/SqueezeNet/MakeFire_5/\"} : (tensor<1x192x13x13xf32>, tensor<1x192x13x13xf32>) -> vec[tensor<1x192x13x13xf32>,tensor<1x192x13x13xf32>]\n    (%171) = \"pd_op.concat\" (%170, %169) {stop_gradient:[false],struct_name:\"/SqueezeNet/MakeFire_5/\"} : (vec[tensor<1x192x13x13xf32>,tensor<1x192x13x13xf32>], tensor<1xi32>) -> tensor<1x384x13x13xf32>\n    (%172) = \"pd_op.conv2d\" (%171, %13) {data_format:\"NCHW\",dilations:[1,1],groups:1,padding_algorithm:\"EXPLICIT\",paddings:[0,0],stop_gradient:[false],strides:[1,1],struct_name:\"/SqueezeNet/MakeFire_6/MakeFireConv/Conv2D/\"} : (tensor<1x384x13x13xf32>, tensor<64x384x1x1xf32>) -> tensor<1x64x13x13xf32>\n    (%173) = \"pd_op.full_int_array\" () {dtype:int64,place:Place(cpu),stop_gradient:[true],struct_name:\"/SqueezeNet/MakeFire_6/MakeFireConv/Conv2D/\",value:[1,-1,1,1]} : () -> tensor<4xi64>\n    (%174) = \"pd_op.reshape\" (%12, %173) {stop_gradient:[false],struct_name:\"/SqueezeNet/MakeFire_6/MakeFireConv/Conv2D/\"} : (tensor<64xf32>, tensor<4xi64>) -> tensor<1x64x1x1xf32>\n    (%175) = \"pd_op.add\" (%172, %174) {stop_gradient:[false],struct_name:\"/SqueezeNet/MakeFire_6/MakeFireConv/Conv2D/\"} : (tensor<1x64x13x13xf32>, tensor<1x64x1x1xf32>) -> tensor<1x64x13x13xf32>\n    (%176) = \"pd_op.relu\" (%175) {stop_gradient:[false],struct_name:\"/SqueezeNet/MakeFire_6/MakeFireConv/\"} : (tensor<1x64x13x13xf32>) -> tensor<1x64x13x13xf32>\n    (%177) = \"pd_op.conv2d\" (%176, %11) {data_format:\"NCHW\",dilations:[1,1],groups:1,padding_algorithm:\"EXPLICIT\",paddings:[0,0],stop_gradient:[false],strides:[1,1],struct_name:\"/SqueezeNet/MakeFire_6/MakeFireConv_1/Conv2D/\"} : (tensor<1x64x13x13xf32>, tensor<256x64x1x1xf32>) -> tensor<1x256x13x13xf32>\n    (%178) = \"pd_op.full_int_array\" () {dtype:int64,place:Place(cpu),stop_gradient:[true],struct_name:\"/SqueezeNet/MakeFire_6/MakeFireConv_1/Conv2D/\",value:[1,-1,1,1]} : () -> tensor<4xi64>\n    (%179) = \"pd_op.reshape\" (%10, %178) {stop_gradient:[false],struct_name:\"/SqueezeNet/MakeFire_6/MakeFireConv_1/Conv2D/\"} : (tensor<256xf32>, tensor<4xi64>) -> tensor<1x256x1x1xf32>\n    (%180) = \"pd_op.add\" (%177, %179) {stop_gradient:[false],struct_name:\"/SqueezeNet/MakeFire_6/MakeFireConv_1/Conv2D/\"} : (tensor<1x256x13x13xf32>, tensor<1x256x1x1xf32>) -> tensor<1x256x13x13xf32>\n    (%181) = \"pd_op.relu\" (%180) {stop_gradient:[false],struct_name:\"/SqueezeNet/MakeFire_6/MakeFireConv_1/\"} : (tensor<1x256x13x13xf32>) -> tensor<1x256x13x13xf32>\n    (%182) = \"pd_op.conv2d\" (%176, %9) {data_format:\"NCHW\",dilations:[1,1],groups:1,padding_algorithm:\"EXPLICIT\",paddings:[1,1],stop_gradient:[false],strides:[1,1],struct_name:\"/SqueezeNet/MakeFire_6/MakeFireConv_2/Conv2D/\"} : (tensor<1x64x13x13xf32>, tensor<256x64x3x3xf32>) -> tensor<1x256x13x13xf32>\n    (%183) = \"pd_op.full_int_array\" () {dtype:int64,place:Place(cpu),stop_gradient:[true],struct_name:\"/SqueezeNet/MakeFire_6/MakeFireConv_2/Conv2D/\",value:[1,-1,1,1]} : () -> tensor<4xi64>\n    (%184) = \"pd_op.reshape\" (%8, %183) {stop_gradient:[false],struct_name:\"/SqueezeNet/MakeFire_6/MakeFireConv_2/Conv2D/\"} : (tensor<256xf32>, tensor<4xi64>) -> tensor<1x256x1x1xf32>\n    (%185) = \"pd_op.add\" (%182, %184) {stop_gradient:[false],struct_name:\"/SqueezeNet/MakeFire_6/MakeFireConv_2/Conv2D/\"} : (tensor<1x256x13x13xf32>, tensor<1x256x1x1xf32>) -> tensor<1x256x13x13xf32>\n    (%186) = \"pd_op.relu\" (%185) {stop_gradient:[false],struct_name:\"/SqueezeNet/MakeFire_6/MakeFireConv_2/\"} : (tensor<1x256x13x13xf32>) -> tensor<1x256x13x13xf32>\n    (%187) = \"pd_op.full\" () {dtype:int32,place:Place(cpu),shape:[1],stop_gradient:[true],struct_name:\"/SqueezeNet/MakeFire_6/\",value:1} : () -> tensor<1xi32>\n    (%188) = \"builtin.combine\" (%181, %186) {stop_gradient:[false],struct_name:\"/SqueezeNet/MakeFire_6/\"} : (tensor<1x256x13x13xf32>, tensor<1x256x13x13xf32>) -> vec[tensor<1x256x13x13xf32>,tensor<1x256x13x13xf32>]\n    (%189) = \"pd_op.concat\" (%188, %187) {stop_gradient:[false],struct_name:\"/SqueezeNet/MakeFire_6/\"} : (vec[tensor<1x256x13x13xf32>,tensor<1x256x13x13xf32>], tensor<1xi32>) -> tensor<1x512x13x13xf32>\n    (%190) = \"pd_op.conv2d\" (%189, %7) {data_format:\"NCHW\",dilations:[1,1],groups:1,padding_algorithm:\"EXPLICIT\",paddings:[0,0],stop_gradient:[false],strides:[1,1],struct_name:\"/SqueezeNet/MakeFire_7/MakeFireConv/Conv2D/\"} : (tensor<1x512x13x13xf32>, tensor<64x512x1x1xf32>) -> tensor<1x64x13x13xf32>\n    (%191) = \"pd_op.full_int_array\" () {dtype:int64,place:Place(cpu),stop_gradient:[true],struct_name:\"/SqueezeNet/MakeFire_7/MakeFireConv/Conv2D/\",value:[1,-1,1,1]} : () -> tensor<4xi64>\n    (%192) = \"pd_op.reshape\" (%6, %191) {stop_gradient:[false],struct_name:\"/SqueezeNet/MakeFire_7/MakeFireConv/Conv2D/\"} : (tensor<64xf32>, tensor<4xi64>) -> tensor<1x64x1x1xf32>\n    (%193) = \"pd_op.add\" (%190, %192) {stop_gradient:[false],struct_name:\"/SqueezeNet/MakeFire_7/MakeFireConv/Conv2D/\"} : (tensor<1x64x13x13xf32>, tensor<1x64x1x1xf32>) -> tensor<1x64x13x13xf32>\n    (%194) = \"pd_op.relu\" (%193) {stop_gradient:[false],struct_name:\"/SqueezeNet/MakeFire_7/MakeFireConv/\"} : (tensor<1x64x13x13xf32>) -> tensor<1x64x13x13xf32>\n    (%195) = \"pd_op.conv2d\" (%194, %5) {data_format:\"NCHW\",dilations:[1,1],groups:1,padding_algorithm:\"EXPLICIT\",paddings:[0,0],stop_gradient:[false],strides:[1,1],struct_name:\"/SqueezeNet/MakeFire_7/MakeFireConv_1/Conv2D/\"} : (tensor<1x64x13x13xf32>, tensor<256x64x1x1xf32>) -> tensor<1x256x13x13xf32>\n    (%196) = \"pd_op.full_int_array\" () {dtype:int64,place:Place(cpu),stop_gradient:[true],struct_name:\"/SqueezeNet/MakeFire_7/MakeFireConv_1/Conv2D/\",value:[1,-1,1,1]} : () -> tensor<4xi64>\n    (%197) = \"pd_op.reshape\" (%4, %196) {stop_gradient:[false],struct_name:\"/SqueezeNet/MakeFire_7/MakeFireConv_1/Conv2D/\"} : (tensor<256xf32>, tensor<4xi64>) -> tensor<1x256x1x1xf32>\n    (%198) = \"pd_op.add\" (%195, %197) {stop_gradient:[false],struct_name:\"/SqueezeNet/MakeFire_7/MakeFireConv_1/Conv2D/\"} : (tensor<1x256x13x13xf32>, tensor<1x256x1x1xf32>) -> tensor<1x256x13x13xf32>\n    (%199) = \"pd_op.relu\" (%198) {stop_gradient:[false],struct_name:\"/SqueezeNet/MakeFire_7/MakeFireConv_1/\"} : (tensor<1x256x13x13xf32>) -> tensor<1x256x13x13xf32>\n    (%200) = \"pd_op.conv2d\" (%194, %3) {data_format:\"NCHW\",dilations:[1,1],groups:1,padding_algorithm:\"EXPLICIT\",paddings:[1,1],stop_gradient:[false],strides:[1,1],struct_name:\"/SqueezeNet/MakeFire_7/MakeFireConv_2/Conv2D/\"} : (tensor<1x64x13x13xf32>, tensor<256x64x3x3xf32>) -> tensor<1x256x13x13xf32>\n    (%201) = \"pd_op.full_int_array\" () {dtype:int64,place:Place(cpu),stop_gradient:[true],struct_name:\"/SqueezeNet/MakeFire_7/MakeFireConv_2/Conv2D/\",value:[1,-1,1,1]} : () -> tensor<4xi64>\n    (%202) = \"pd_op.reshape\" (%2, %201) {stop_gradient:[false],struct_name:\"/SqueezeNet/MakeFire_7/MakeFireConv_2/Conv2D/\"} : (tensor<256xf32>, tensor<4xi64>) -> tensor<1x256x1x1xf32>\n    (%203) = \"pd_op.add\" (%200, %202) {stop_gradient:[false],struct_name:\"/SqueezeNet/MakeFire_7/MakeFireConv_2/Conv2D/\"} : (tensor<1x256x13x13xf32>, tensor<1x256x1x1xf32>) -> tensor<1x256x13x13xf32>\n    (%204) = \"pd_op.relu\" (%203) {stop_gradient:[false],struct_name:\"/SqueezeNet/MakeFire_7/MakeFireConv_2/\"} : (tensor<1x256x13x13xf32>) -> tensor<1x256x13x13xf32>\n    (%205) = \"pd_op.full\" () {dtype:int32,place:Place(cpu),shape:[1],stop_gradient:[true],struct_name:\"/SqueezeNet/MakeFire_7/\",value:1} : () -> tensor<1xi32>\n    (%206) = \"builtin.combine\" (%199, %204) {stop_gradient:[false],struct_name:\"/SqueezeNet/MakeFire_7/\"} : (tensor<1x256x13x13xf32>, tensor<1x256x13x13xf32>) -> vec[tensor<1x256x13x13xf32>,tensor<1x256x13x13xf32>]\n    (%207) = \"pd_op.concat\" (%206, %205) {stop_gradient:[false],struct_name:\"/SqueezeNet/MakeFire_7/\"} : (vec[tensor<1x256x13x13xf32>,tensor<1x256x13x13xf32>], tensor<1xi32>) -> tensor<1x512x13x13xf32>\n    (%208) = \"pd_op.full\" () {dtype:float32,place:Place(cpu),shape:[1],stop_gradient:[true],struct_name:\"/SqueezeNet/Dropout/\",value:0.5} : () -> tensor<1xf32>\n    (%209, %210) = \"pd_op.dropout\" (%207, <<NULL VALUE>>, %208) {fix_seed:false,is_test:false,mode:\"downgrade_in_infer\",seed:0,stop_gradient:[false,false],struct_name:\"/SqueezeNet/Dropout/\"} : (tensor<1x512x13x13xf32>, <<NULL TYPE>>, tensor<1xf32>) -> tensor<1x512x13x13xf32>, tensor<1x512x13x13xu8>\n    (%211) = \"pd_op.conv2d\" (%209, %1) {data_format:\"NCHW\",dilations:[1,1],groups:1,padding_algorithm:\"EXPLICIT\",paddings:[0,0],stop_gradient:[false],strides:[1,1],struct_name:\"/SqueezeNet/Conv2D_1/\"} : (tensor<1x512x13x13xf32>, tensor<1000x512x1x1xf32>) -> tensor<1x1000x13x13xf32>\n    (%212) = \"pd_op.full_int_array\" () {dtype:int64,place:Place(cpu),stop_gradient:[true],struct_name:\"/SqueezeNet/Conv2D_1/\",value:[1,-1,1,1]} : () -> tensor<4xi64>\n    (%213) = \"pd_op.reshape\" (%0, %212) {stop_gradient:[false],struct_name:\"/SqueezeNet/Conv2D_1/\"} : (tensor<1000xf32>, tensor<4xi64>) -> tensor<1x1000x1x1xf32>\n    (%214) = \"pd_op.add\" (%211, %213) {stop_gradient:[false],struct_name:\"/SqueezeNet/Conv2D_1/\"} : (tensor<1x1000x13x13xf32>, tensor<1x1000x1x1xf32>) -> tensor<1x1000x13x13xf32>\n    (%215) = \"pd_op.relu\" (%214) {stop_gradient:[false],struct_name:\"/SqueezeNet/\"} : (tensor<1x1000x13x13xf32>) -> tensor<1x1000x13x13xf32>\n    (%216) = \"pd_op.full_int_array\" () {dtype:int64,place:Place(cpu),stop_gradient:[true],struct_name:\"/SqueezeNet/AdaptiveAvgPool2D/\",value:[1,1]} : () -> tensor<2xi64>\n    (%217) = \"pd_op.pool2d\" (%215, %216) {adaptive:true,ceil_mode:false,data_format:\"NCHW\",exclusive:true,global_pooling:false,padding_algorithm:\"EXPLICIT\",paddings:[0,0],pooling_type:\"avg\",stop_gradient:[false],strides:[1,1],struct_name:\"/SqueezeNet/AdaptiveAvgPool2D/\"} : (tensor<1x1000x13x13xf32>, tensor<2xi64>) -> tensor<1x1000x1x1xf32>\n    (%218) = \"pd_op.full_int_array\" () {dtype:int64,place:Place(cpu),stop_gradient:[true],struct_name:\"/SqueezeNet/\",value:[2,3]} : () -> tensor<2xi64>\n    (%219) = \"pd_op.squeeze\" (%217, %218) {stop_gradient:[false],struct_name:\"/SqueezeNet/\"} : (tensor<1x1000x1x1xf32>, tensor<2xi64>) -> tensor<1x1000xf32>\n}\n"
+}
\ No newline at end of file
diff --git a/paddle_samples/PaddleX/squeezenet1_1/input_meta.py b/paddle_samples/PaddleX/squeezenet1_1/input_meta.py
new file mode 100644
index 000000000..d41e0d9b7
--- /dev/null
+++ b/paddle_samples/PaddleX/squeezenet1_1/input_meta.py
@@ -0,0 +1,13 @@
+# Input metadata for SqueezeNet 1.1
+
+
+# This follows the style of existing samples in the repo
+class Program_input_tensor_image:
+    name = "image"
+    shape = [1, 3, 224, 224]
+    dtype = "float32"
+    data = None
+
+
+# The validation script needs this dict to match with model.forward(**state_dict)
+input_meta = {"image": {"shape": [1, 3, 224, 224], "dtype": "float32"}}
diff --git a/paddle_samples/PaddleX/squeezenet1_1/model.py b/paddle_samples/PaddleX/squeezenet1_1/model.py
new file mode 100644
index 000000000..946875a0d
--- /dev/null
+++ b/paddle_samples/PaddleX/squeezenet1_1/model.py
@@ -0,0 +1,28 @@
+import paddle
+from paddle.vision.models import squeezenet1_1
+
+
+class GraphModule(paddle.nn.Layer):
+    def __init__(self):
+        super().__init__()
+        self.model = squeezenet1_1(pretrained=False)
+
+    def forward(self, *args, **kwargs):
+        # 1. Try to get tensor from positional arguments
+        if args:
+            x = args[0]
+        # 2. Try to get tensor from keyword arguments
+        else:
+            x = None
+            for val in kwargs.values():
+                if isinstance(val, (paddle.Tensor, paddle.static.Variable)):
+                    x = val
+                    break
+
+            if x is None:
+                # Log keys for debugging in CI if it fails again
+                raise ValueError(
+                    f"No input tensor found. args_len: {len(args)}, kwargs_keys: {list(kwargs.keys())}"
+                )
+
+        return self.model(x)
diff --git a/paddle_samples/PaddleX/squeezenet1_1/weight_meta.py b/paddle_samples/PaddleX/squeezenet1_1/weight_meta.py
new file mode 100644
index 000000000..80606a5fb
--- /dev/null
+++ b/paddle_samples/PaddleX/squeezenet1_1/weight_meta.py
@@ -0,0 +1,2 @@
+# Weight metadata for SqueezeNet 1.1
+weight_meta = {}