code: def num_sampled_nodes(self, i):
      return (
          self.cum_sampled_nodes[i] / self.num_steps
          if self.w >= 1
          else self.cum_sampled_nodes[i]
          * (1 - self.w)
          / (1 - self.w**self.num_steps)
      )
  
imports: import argparse
  import glob
  import math
  import os
  import time
  import dgl
  import torch
  import torch.nn
  import torch.nn.functional
  from ladies_sampler import LadiesSampler, normalized_edata, PoissonLadiesSampler
  from load_graph import load_dataset
  from model import SAGE
  from pytorch_lightning import LightningDataModule, LightningModule, Trainer
  from pytorch_lightning.callbacks import Callback, EarlyStopping, ModelCheckpoint
  from pytorch_lightning.loggers import TensorBoardLogger
  from torchmetrics.classification import MulticlassF1Score, MultilabelF1Score
label_desc: Use image node_id 2 for calling the SAGELightning obj's underlying member method code with example usage: obj.num_sampled_nodes(i) and returns: unknown