Project: s41467-025-58055-3 | Function: train_dyfl_vit (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 dynamic federated learning training for Vision Transformer models with resource-adaptive capabilities. Handles heterogeneous computational resources across nodes, manages data distribution, applies gradient updates, and tracks training metrics including loss and accuracy for fair federated learning.

**KEY TERMS:** {
  "node.model": "federated client model",
  "train_loader": "local training data",
  "loss_fn": "federated learning loss function",
  "node.optimizer": "local optimization algorithm"
}

**CONTEXT CODE:** import copy
import time

import torch
import torch.nn as nn
import torch.nn.functional as F
from tqdm import tqdm

from models.builder import build_model
from models.dbb_block import DiverseBranchBlock
from models.dyrep import DyRep, build_optimizer
from models.recal_bn import recal_bn
from utils.misc import AverageMeter, accuracy


def train_dyfl_vit(node, epoch, round):
    """
    Implements dynamic federated learning training for Vision Transformer models with resource-adaptive capabilities.

    This function handles heterogeneous computational resources across nodes, manages data distribution,
    applies gradient updates, and tracks training metrics including loss and accuracy for fair federated learning.

    The implementation corresponds to the resource-adaptive framework described in the paper that
    dynamically adjusts to varying computational capacities, ensuring fair participation across
    heterogeneous clients in federated healthcare scenarios.

    Args:
        node: Federated learning node with model, data, optimizer, and device configuration
        epoch: Current training epoch number
        round: Current federated learning round number

    Key algorithmic elements:
    - Training loops with progress tracking
    - Gradient computation and backpropagation
    - Loss and accuracy metric tracking
    - Device-specific model management
    - Heterogeneous data handling for medical datasets

    Paper reference: "Towards fairness-aware and privacy-preserving enhanced collaborative learning for healthcare"
    Formula: Federated learning with resource-adaptive model scaling
    """
    # MASKED: Dynamic federated learning Vision Transformer training implementation
    raise NotImplementedError("This function is masked for testing purposes. The function implements dynamic federated learning training for Vision Transformer models with resource-adaptive capabilities, handling heterogeneous computational resources across nodes, managing data distribution, applying gradient updates, and tracking training metrics including loss and accuracy for fair federated learning.")



def train_dyfl(node, epoch, round):
    node.interaction_model.to(node.device).train()
    train_loader = node.train_data
    loss_fn = nn.CrossEntropyLoss()
    loss_m = AverageMeter()
    val_loss_fn = loss_fn
    total_loss = 0.0
    avg_loss = 0.0
    correct = 0.0
    acc = 0.0
    dyrep = DyRep( node.interaction_model,
                    node.optimizer,
                    recal_bn_fn=lambda m: recal_bn(node.interaction_model, train_loader,
                    node.args.dyrep_recal_bn_iters, m, node.device),
                    filter_bias_and_bn=not node.args.opt_no_filter, device = node.device)
    description = "Node{:d}: loss={:.4f} acc={:.2f}%"
    with tqdm(train_loader) as epochs:
        for batch_idx, (data, target) in enumerate(epochs):
            epochs.set_description(description.format(node.num, avg_loss, acc))
            data = data.float().to(node.device)
            if target.dim() > 1 and target.size(1) == 1:
                target = target.long().to(node.device).squeeze(dim=1)
            else:
                target = target.long().to(node.device)
            # target = target.long().to(node.device).squeeze()
            for p in node.interaction_model.parameters():
                p.grad = None
            output = node.interaction_model(data)

            loss = loss_fn(output, target)
            loss.backward()
            dyrep.record_metrics()
            node.optimizer.step()
            loss_m.update(loss.item(), n=data.size(0))
            total_loss += loss
            avg_loss = total_loss / (batch_idx + 1)
            pred = output.argmax(dim=1)
            correct += pred.eq(target.view_as(pred)).sum()
            acc = correct / len(train_loader.dataset) * 100
    

    if epoch ==0:
        device_capacity = node.capacity
        dyrep.adjust_model(device_capacity)


def validate(args, epoch, model, loader, loss_fn, log_suffix=''):
    loss_m = AverageMeter()
    top1_m = AverageMeter()
    top5_m = AverageMeter()
    batch_time_m = AverageMeter()
    start_time = time.time()

    model.eval()
    for batch_idx, (input, target) in enumerate(loader):
        input = input.float().to(args.device)
        target = target.long().to(args.device)
        with torch.no_grad():
            output = model(input)
            loss = loss_fn(output, target)

        top1, top5 = accuracy(output, target, topk=(1, 5))
        loss_m.update(loss.item(), n=input.size(0))
        top1_m.update(top1 * 100, n=input.size(0))
        top5_m.update(top5 * 100, n=input.size(0))

        batch_time = time.time() - start_time
        batch_time_m.update(batch_time)



Complete the function implementation replacing "raise NotImplementedError".