code: def create_validation_data_loader(
      self,
      data: Dataset,
      **kwargs,
  ) -> DataLoader:
      input_names = get_hybrid_forward_input_names(
          SelfAttentionTrainingNetwork
      )
      instance_splitter = self._create_instance_splitter("validation")
      return ValidationDataLoader(
          dataset=data,
          transform=instance_splitter + SelectFields(input_names),
          batch_size=self.batch_size,
          stack_fn=partial(
              batchify, ctx=self.trainer.ctx, dtype=self.dtype
          ),
      )
  
imports: from functools import partial
  from typing import List, Optional
  from mxnet.gluon import HybridBlock
  from gluonts.core.component import validated
  from gluonts.dataset.common import Dataset
  from gluonts.dataset.field_names import FieldName
  from gluonts.dataset.loader import DataLoader, TrainDataLoader, ValidationDataLoader
  from gluonts.model.forecast_generator import QuantileForecastGenerator
  from gluonts.mx.batchify import batchify
  from gluonts.mx.model.estimator import GluonEstimator
  from gluonts.mx.model.predictor import RepresentableBlockPredictor
  from gluonts.mx.trainer import Trainer
  from gluonts.mx.util import copy_parameters, get_hybrid_forward_input_names
  from gluonts.time_feature import TimeFeature, time_features_from_frequency_str
  from gluonts.transform import AddAgeFeature, AddObservedValuesIndicator, AddTimeFeatures, AsNumpyArray, Chain, ExpectedNumInstanceSampler, InstanceSampler, InstanceSplitter, SelectFields, SetField, TestSplitSampler, Transformation, ValidationSplitSampler, VstackFeatures
  from ._network import SelfAttentionPredictionNetwork, SelfAttentionTrainingNetwork
label_desc: Use image node_id 5 for calling the SelfAttentionEstimator obj's underlying member method code with example usage: obj.create_validation_data_loader(data) and returns: ValidationDataLoader