code: def evaluate(self, dataloader):
      self.model.eval()
      with torch.no_grad():
          pred_collect = []
          gt_collect = []
          for data in dataloader:
              tgt = to_cuda(data["tgt_tensor"], self.config["device"])
              data["graph_data"] = data["graph_data"].to(
                  self.config["device"]
              )
              logits = self.model(
                  data["graph_data"], require_loss=False
              )
              pred_collect.append(logits)
              gt_collect.append(tgt)
  
          pred_collect = torch.max(torch.cat(pred_collect, 0), dim=-1)[
              1
          ].cpu()
          gt_collect = torch.cat(gt_collect, 0).cpu()
          score = self.metric.calculate_scores(
              ground_truth=gt_collect, predict=pred_collect
          )[0]
  
          return score
  
imports: import argparse
  import os
  import time
  import numpy
  import torch
  import torch.backends.cudnn
  import torch.multiprocessing
  import torch.nn
  import torch.nn.functional
  import torch.optim
  import yaml
  from torch.optim.lr_scheduler import ReduceLROnPlateau
  from torch.utils.data import DataLoader
  from graph4nlp.pytorch.datasets.trec import TrecDataset
  from graph4nlp.pytorch.modules.evaluation.accuracy import Accuracy
  from graph4nlp.pytorch.modules.graph_construction import NodeEmbeddingBasedGraphConstruction, NodeEmbeddingBasedRefinedGraphConstruction
  from graph4nlp.pytorch.modules.graph_embedding_initialization.embedding_construction import WordEmbedding
  from graph4nlp.pytorch.modules.graph_embedding_initialization.graph_embedding_initialization import GraphEmbeddingInitialization
  from graph4nlp.pytorch.modules.graph_embedding_learning import GAT, GGNN, GraphSAGE
  from graph4nlp.pytorch.modules.loss.general_loss import GeneralLoss
  from graph4nlp.pytorch.modules.prediction.classification.graph_classification import FeedForwardNN
  from graph4nlp.pytorch.modules.utils import constants
  from graph4nlp.pytorch.modules.utils.generic_utils import EarlyStopping, grid, to_cuda
  from graph4nlp.pytorch.modules.utils.logger import Logger
label_desc: Use image node_id 7 for calling the ModelHandler obj's underlying member method code with example usage: obj.evaluate(dataloader) and returns: score