code: def __init__(
      self,
      freq: str,
      prediction_length: int,
      trainer: Trainer = Trainer(),
      context_length: Optional[int] = None,
      num_layers: int = 2,
      num_cells: int = 40,
      cell_type: str = "lstm",
      dropoutcell_type: str = "ZoneoutCell",
      dropout_rate: float = 0.1,
      use_feat_dynamic_real: bool = False,
      use_feat_static_cat: bool = False,
      use_feat_static_real: bool = False,
      cardinality: Optional[List[int]] = None,
      embedding_dimension: Optional[List[int]] = None,
      distr_output: DistributionOutput = StudentTOutput(),
      scaling: bool = True,
      lags_seq: Optional[List[int]] = None,
      time_features: Optional[List[TimeFeature]] = None,
      num_parallel_samples: int = 100,
      imputation_method: Optional[MissingValueImputation] = None,
      train_sampler: Optional[InstanceSampler] = None,
      validation_sampler: Optional[InstanceSampler] = None,
      dtype: Type = np.float32,
      alpha: float = 0.0,
      beta: float = 0.0,
      batch_size: int = 32,
      default_scale: Optional[float] = None,
      minimum_scale: float = 1e-10,
      impute_missing_values: bool = False,
      num_imputation_samples: int = 1,
      history_length: Optional[int] = None,
  ) -> None:
      super().__init__(
          freq=freq,
          prediction_length=prediction_length,
          trainer=trainer,
          context_length=context_length,
          num_layers=num_layers,
          num_cells=num_cells,
          cell_type=cell_type,
          dropoutcell_type=dropoutcell_type,
          dropout_rate=dropout_rate,
          use_feat_dynamic_real=use_feat_dynamic_real,
          use_feat_static_cat=use_feat_static_cat,
          use_feat_static_real=use_feat_static_real,
          cardinality=cardinality,
          embedding_dimension=embedding_dimension,
          distr_output=distr_output,
          scaling=scaling,
          lags_seq=lags_seq,
          time_features=time_features,
          num_parallel_samples=num_parallel_samples,
          imputation_method=imputation_method,
          train_sampler=train_sampler,
          validation_sampler=validation_sampler,
          alpha=alpha,
          beta=beta,
          default_scale=default_scale,
          minimum_scale=minimum_scale,
          impute_missing_values=impute_missing_values,
          num_imputation_samples=num_imputation_samples,
          batch_size=batch_size,
          dtype=dtype,
      )
      self.freq = freq
      self.history_length: int = (
          history_length
          if history_length is not None
          else self.history_length
      )
  
imports: from functools import partial
  from typing import Callable, Iterator, List, Optional
  from pathlib import Path
  import mxnet
  import numpy
  from gluonts.core.component import Type, validated
  from gluonts.core.serde import load_json
  from gluonts.dataset.common import DataEntry, Dataset
  from gluonts.dataset.loader import DataBatch, InferenceDataLoader
  from gluonts.model.forecast import Forecast
  from gluonts.model.forecast_generator import ForecastGenerator, SampleForecastGenerator
  from gluonts.mx.batchify import stack
  from gluonts.mx.context import get_mxnet_context
  from gluonts.mx.distribution import DistributionOutput, StudentTOutput
  from gluonts.mx.model.deepar import DeepAREstimator
  from gluonts.mx.model.deepar._network import DeepARPredictionNetwork
  from gluonts.mx.model.predictor import RepresentableBlockPredictor
  from gluonts.mx.trainer import Trainer
  from gluonts.mx.util import import_repr_block
  from gluonts.time_feature import TimeFeature
  from gluonts.transform import InstanceSampler, Transformation
  from gluonts.transform.feature import MissingValueImputation
label_desc: Use image node_id 1 to create a new DeepAREstimatorForCOP object from inherited base classes: DeepAREstimator with example: obj = DeepAREstimatorForCOP(freq, prediction_length, trainer, context_length, num_layers, num_cells, cell_type, dropoutcell_type, dropout_rate, use_feat_dynamic_real, use_feat_static_cat, use_feat_static_real, cardinality, embedding_dimension, distr_output, scaling, lags_seq, time_features, num_parallel_samples, imputation_method, train_sampler, validation_sampler, dtype, alpha, beta, batch_size, default_scale, minimum_scale, impute_missing_values, num_imputation_samples, history_length)