code: def create_training_network(self) -> SelfAttentionTrainingNetwork:
      return SelfAttentionTrainingNetwork(
          context_length=self.context_length,
          prediction_length=self.prediction_length,
          d_hidden=self.model_dim,
          m_ffn=self.ffn_dim_multiplier,
          n_head=self.num_heads,
          n_layers=self.num_layers,
          n_output=self.num_outputs,
          cardinalities=self.cardinalities,
          kernel_sizes=self.kernel_sizes,
          dist_enc=self.distance_encoding,
          pre_ln=self.pre_layer_norm,
          dropout=self.dropout,
          temperature=self.temperature,
      )
  
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 6 for calling the SelfAttentionEstimator obj's underlying member method code with example usage: obj.create_training_network() and returns: SelfAttentionTrainingNetwork