model-zoo error about Breast density classification execute reasoning

hello, My question is as follows

I have downloaded model.pth from the address。

code:

`import glob
import os
import torch
import matplotlib.pyplot as plt
from PIL import Image
from torchvision import transforms
from torchvision.models import inception_v3
from monai.bundle import download, load

data_dir = 'breast_density_classification/sample_data'

test_images = sorted(glob.glob(os.path.join(data_dir, "A", "*.jpg")))

preprocess = transforms.Compose([
transforms.Resize((299, 299)),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
])

device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")

model_path = "breast_density_classification/models/model.pth"

model = inception_v3(pretrained=False, aux_logits=False, num_classes=4).to(device)

state_dict = torch.load(model_path, map_location=device)
model.load_state_dict(state_dict)
model.eval()

for img_path in test_images:
img = Image.open(img_path).convert('RGB')
img_tensor = preprocess(img).unsqueeze(0).to(device)

with torch.no_grad():
    outputs = model(img_tensor)
    probs = torch.nn.functional.softmax(outputs, dim=1)
    pred_class = torch.argmax(probs, dim=1).item()

plt.figure(figsize=(6, 6))
plt.imshow(img)
plt.title(f'Predicted Class: {pred_class}')
plt.axis('off')
plt.show()`

error:

raise RuntimeError('Error(s) in loading state_dict for {}:\n\t{}'.format(
RuntimeError: Error(s) in loading state_dict for Inception3:
Missing key(s) in state_dict: "Conv2d_1a_3x3.conv.weight", "Conv2d_1a_3x3.bn.weight",

This error indicates that the loaded state_dict does not match the defined InceptionV3 model. Did I do something wrong.
This is an urgent problem. thank you

Start with the reported model.pth checkpoint and the breast_density_classification loading example in the issue. Compare the checkpoint format with the torchvision Inception V3 definition and the model zoo's intended loading path. Done means reproducing the state-dict mismatch and documenting or correcting the supported way to load this model.