code: def filter_schemas(self):
      targets = None
      train_schema = None
      if self.train_ds:
          train_schema = self.train_ds.schema
          targets = self.get_targets(train_schema)
  
      eval_schema = None
      if self.eval_ds:
          eval_schema = self.eval_ds.schema
          targets = self.get_targets(eval_schema)
  
      if targets and "all" not in self.args.tasks:
          flattened_targets = [y for x in targets.values() for y in x]
          # Removing from schema targets not used
          targets_to_remove = list(
              set(
                  (train_schema or eval_schema)
                  .select_by_tag(Tags.TARGET)
                  .column_names
              ).difference(set(flattened_targets))
          )
  
          if train_schema:
              train_schema = self.filter_columns(train_schema)
              train_schema = train_schema.excluding_by_name(
                  targets_to_remove
              )
          if eval_schema:
              eval_schema = self.filter_columns(eval_schema)
              eval_schema = eval_schema.excluding_by_name(
                  targets_to_remove
              )
  
      schema = train_schema or eval_schema
      if not schema and self.predict_ds:
          schema = self.predict_ds.schema
      return schema, eval_schema, targets
  
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 5 for calling the RankingTrainEvalRunner obj's underlying member method code with example usage: obj.filter_schemas() and returns: schema,  eval_schema,  targets