Project: s41467-024-46700-2 | Function: infer_csv_file (python)

system: You are an expert Python programmer implementing algorithms from scientific papers.
Complete the masked function based on the algorithm description.
Follow Python best practices and maintain consistent code style.
Return **only** the complete Python function code.

user: Complete this Python function:

**DESCRIPTION:** Implements deep learning ensemble model inference for ultrasound-based ovarian cancer prediction using pre-trained CNN models with sigmoid probability output for multimodal integration

**KEY TERMS:** {
  "learn": "fastai learner object with pre-trained model",
  "get_preds": "inference function for test dataset",
  "sigmoid": "activation function for probability output",
  "np_preds": "numpy array of ensemble predictions",
  "DL_preds": "deep learning prediction scores for OvcaFinder input",
  "ImageList": "fastai data structure for image processing"
}

**CONTEXT CODE:** from fastai.vision import *
import os
import pandas as pd

from train_DL_model import mean_absolute_error_, SaveCallback


def infer_csv_file(model_name, csv_add, sv_add=None):
    # MASKED: Implements deep learning ensemble model inference for ultrasound-based ovarian cancer prediction using pre-trained CNN models with sigmoid probability output for multimodal integration
    raise NotImplementedError("This function is masked for testing purposes.")


test_result_dir = './results'

model_dict = [
    ['ckpt/densenet121/', '{model file name}.pkl'],
    ['ckpt/densenet169', '{model file file name}.pkl'],
    ['ckpt/densenet201/', '{model name}.pkl'],
    ['ckpt/resnet34/', '{model file name}.pkl'],
    ['ckpt/efficientnet_b5/', '{model file name}.pkl'],
    ['ckpt/efficientnet_b6/', '{model file name}.pkl'],
]

if __name__ == "__main__":
    for i in range(len(model_dict)):
        model_path = model_dict[i][0]
        model_name = model_dict[i][1]
        infer_csv_file(model_name, "./data/test.csv")


Complete the function implementation replacing "raise NotImplementedError".