code: def set_dataloaders(self, train_schema, eval_schema):
      args = self.args
  
      self.train_loader = None
      if self.train_ds:
          train_loader_kwargs = {}
          if self.args.in_batch_negatives_train:
              train_loader_kwargs["transform"] = InBatchNegatives(
                  train_schema, args.in_batch_negatives_train
              )
          self.train_loader = mm.Loader(
              self.train_ds,
              batch_size=args.train_batch_size,
              schema=train_schema,
              shuffle=args.shuffled_train,
              **train_loader_kwargs,
          )
  
      self.eval_loader = None
      if self.eval_ds:
          eval_loader_kwargs = {}
          if args.in_batch_negatives_eval:
              eval_loader_kwargs["transform"] = InBatchNegatives(
                  eval_schema, args.in_batch_negatives_eval
              )
  
          self.eval_loader = mm.Loader(
              self.eval_ds,
              batch_size=args.eval_batch_size,
              schema=eval_schema,
              shuffle=False,
              **eval_loader_kwargs,
          )
  
      self.predict_loader = None
      if self.predict_ds:
          self.predict_loader = mm.Loader(
              self.predict_ds,
              batch_size=args.eval_batch_size,
              shuffle=False,
          )
  
imports: import json
  import logging
  import os
  from datetime import datetime
  import merlin.models.tf
  import numpy
  import tensorflow
  from merlin.io.dataset import Dataset
  from merlin.models.tf.logging.callbacks import ExamplesPerSecondCallback, WandbLogger
  from merlin.models.tf.transforms.negative_sampling import InBatchNegatives
  from merlin.schema.tags import Tags
  from .args_parsing import Task, parse_arguments
  from .mtl import get_mtl_loss_weights, get_mtl_prediction_tasks
  from .ranking_models import get_model
label_desc: Use image node_id 6 for calling the RankingTrainEvalRunner obj's underlying member method code with example usage: obj.set_dataloaders(train_schema, eval_schema) without return types