Project: 022_BIRD_A_Trustworthy_Bayesian_Inference_Framework_fo | Function: forward (iclr)

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:** Core Bayesian network probability calculation using Bordley's multiplicative formula for copyright-protecting model fusion

**KEY TERMS:** {}

**CONTEXT CODE:**     structure_list_flat = []
    count_num = 0
    for key, value in structured_factors.items():
        structure_list_temp = []
        for v in value:
            structure_list_temp.append(v)
            structure_list_flat.append(v)
            count_num += 1
        structure_list.append(structure_list_temp)

    def dynloop(data, cur_y=0, final_lst=[], temp_lst=[]):
        max_y = len(data) - 1
        for x in range(len(data[cur_y])):
            temp_lst.append(data[cur_y][x])
            if cur_y == max_y:
                final_lst.append([*temp_lst])
            else:
                dynloop(data, cur_y + 1, final_lst, temp_lst)

            temp_lst.pop()

        return final_lst

    complete_test = dynloop(structure_list)

    prediction_x = []
    prediction_y = []

    for sample in complete_test:
        sent_index = [structure_list_flat.index(sent) for sent in sample]

        def evaluate(i):
            if i in sent_index:
                return 1
            else:
                return 0

        x = [evaluate(i) for i in range(len(structure_list_flat))]

        prediction_x.append(x)
        prediction_y.append(1)

    return structure_list_flat, complete_test, prediction_x, prediction_y


class Probnetwork(nn.Module):
    def __init__(self, input_prob):
        super(Probnetwork, self).__init__()
        self.para = nn.Parameter(torch.tensor(input_prob))

    def forward(self, x):
        raise NotImplementedError("Function implementation has been masked for testing")
    
def calculate_mse_loss(outputs, target):
    mse_loss = nn.MSELoss()
    output = mse_loss(outputs, target)
    return output


def calculate_margin_loss(para, origin, target):
    # para = torch.Tensor([0.75, 0.05, 0.75, 0.25, 0.75, 0.5, 0.25])
    # origin =  torch.Tensor([0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5])
    # target = torch.LongTensor([1,-1,1,-1,1,1,-1])

    margin_loss = nn.MarginRankingLoss()
    output = margin_loss(para, origin, target)
    return output


def parse_args():
    parser = argparse.ArgumentParser()
    parser.add_argument("--model_name", type=str, default="meta-llama/Llama-3.1-70B-Instruct", help="select the model name")
    parser.add_argument("--dataset_name", type=str, default="plasma", help="dataset name")
    parser.add_argument("--save_file_dic", type=str, default="/shared/fengyu/BIRD/results/", help="save file dictionary")
    parser.add_argument("--sample_num", type=int, default=128, help="sample size")
    parser.add_argument("--use_temp", type=int, default=0.7, help="LLM's temperature")
    parser.add_argument("--batch_size", type=int, default=4, help="batch size")
    parser.add_argument("--epoch", type=int, default=20, help="training epoch")
    parser.add_argument("--lr", type=int, default=1e-2, help="learning rate")
    parser.add_argument("--start", type=int, default=0, help="start instance")
    parser.add_argument("--end", type=int, default=10000, help="end instance")

    args = parser.parse_args()
    return args


if __name__ == '__main__':
    args = parse_args()
    print(args)



    with open(args.save_file_dic + args.dataset_name + "_" + args.model_name.replace("/", "-") + "_0_w_factors.json") as f:
        df_list = json.load(f)

    out_objs = []
    for i, df in enumerate(df_list):
        if i < args.start:
            continue
        if i >= args.end:
            break
        print(i)
        scenario = df['scenario']
        outcome1 = df['statement']
        outcome2 = df['opposite_statement']
        structured_factors = df['structured_factors']
        factor_outcome_mapping = df['factor_outcome_mapping']

        if len(structured_factors) == 0:
            objs = {
                "scenario": scenario,
                "statement": outcome1,
                "opposite_statement": outcome2,
                "structured_factors": df['structured_factors'],
                "sampled_train": [],
                "train_x": [],
                "train_y": [],
                "para_prob": [],

            }

            out_objs.append(objs)
            continue

        def get_initial_prob(structured_factors, factor_outcome_mapping):
            initial_prob = []
            for key, value in structured_factors.items():
                for v in value:
                    mapped_value = factor_outcome_mapping[v]
                    if mapped_value == 'Statement 1':
                        initial_prob.append(0.75)
                    elif mapped_value == 'Statement 2':
                        initial_prob.append(0.25)
                    elif mapped_value == 'Undecided' or mapped_value == 'Neutral':
                        initial_prob.append(0.5)
            return initial_prob
        initial_prob = get_initial_prob(structured_factors, factor_outcome_mapping)



        origin = torch.Tensor([0.5] * len(initial_prob))
        def evaluate(i):
            if i - 0.5 > 0:
                return 1
            elif i - 0.5 < 0:
                return -1
            elif i - 0.5 == 0:
                return 0
        target = torch.LongTensor([evaluate(i) for i in initial_prob])



Complete the function implementation replacing "raise NotImplementedError".