code: def __init__(
      self,
      freq: str,
      prediction_length: int,
      cardinalities: List[int] = [],
      context_length: Optional[int] = None,
      trainer: Trainer = Trainer(),
      model_dim: int = 64,
      ffn_dim_multiplier: int = 2,
      num_heads: int = 4,
      num_layers: int = 3,
      num_outputs: int = 3,
      kernel_sizes: List[int] = [3, 5, 7, 9],
      distance_encoding: Optional[str] = "dot",
      pre_layer_norm: bool = False,
      dropout: float = 0.1,
      temperature: float = 1.0,
      time_features: Optional[List[TimeFeature]] = None,
      use_feat_dynamic_real: bool = True,
      use_feat_dynamic_cat: bool = False,
      use_feat_static_real: bool = False,
      use_feat_static_cat: bool = True,
      train_sampler: Optional[InstanceSampler] = None,
      validation_sampler: Optional[InstanceSampler] = None,
      batch_size: int = 32,
  ):
      super().__init__(trainer=trainer, batch_size=batch_size)
  
      self.prediction_length = prediction_length
      self.context_length = context_length or prediction_length
      self.model_dim = model_dim
      self.ffn_dim_multiplier = ffn_dim_multiplier
      self.num_heads = num_heads
      self.num_layers = num_layers
      self.num_outputs = num_outputs
      self.cardinalities = cardinalities
      self.kernel_sizes = kernel_sizes
      self.distance_encoding = distance_encoding
      self.pre_layer_norm = pre_layer_norm
      self.dropout = dropout
      self.temperature = temperature
  
      self.time_features = (
          time_features or time_features_from_frequency_str(freq)
      )
      self.use_feat_dynamic_cat = use_feat_dynamic_cat
      self.use_feat_dynamic_real = use_feat_dynamic_real
      self.use_feat_static_cat = use_feat_static_cat
      self.use_feat_static_real = use_feat_static_real
      self.train_sampler = (
          train_sampler
          if train_sampler is not None
          else ExpectedNumInstanceSampler(
              num_instances=1.0, min_future=prediction_length
          )
      )
      self.validation_sampler = (
          validation_sampler
          if validation_sampler is not None
          else ValidationSplitSampler(min_future=prediction_length)
      )
  
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 1 to create a new SelfAttentionEstimator object from inherited base classes: GluonEstimator with example: obj = SelfAttentionEstimator(freq, prediction_length, cardinalities, context_length, trainer, model_dim, ffn_dim_multiplier, num_heads, num_layers, num_outputs, kernel_sizes, distance_encoding, pre_layer_norm, dropout, temperature, time_features, use_feat_dynamic_real, use_feat_dynamic_cat, use_feat_static_real, use_feat_static_cat, train_sampler, validation_sampler, batch_size)