code: def __init__(
      self,
      freq: str,
      prediction_length: int,
      cardinality: List[int],
      embedding_dimension: int,
      decoder_mlp_layer: List[int],
      decoder_mlp_static_dim: int,
      scaler: Scaler = NOPScaler(),
      context_length: Optional[int] = None,
      quantiles: Optional[List[float]] = None,
      trainer: Trainer = Trainer(),
      num_parallel_samples: int = 100,
  ) -> None:
      encoder = HierarchicalCausalConv1DEncoder(
          dilation_seq=[1, 3, 9],
          kernel_size_seq=([3] * len([30, 30, 30])),
          channels_seq=[30, 30, 30],
          use_residual=True,
          use_dynamic_feat=True,
          use_static_feat=True,
      )
  
      super().__init__(
          freq=freq,
          prediction_length=prediction_length,
          encoder=encoder,
          cardinality=cardinality,
          embedding_dimension=embedding_dimension,
          decoder_mlp_layer=decoder_mlp_layer,
          decoder_mlp_static_dim=decoder_mlp_static_dim,
          context_length=context_length,
          scaler=scaler,
          quantiles=quantiles,
          trainer=trainer,
          num_parallel_samples=num_parallel_samples,
      )
  
imports: from functools import partial
  from typing import List, Optional
  import mxnet
  from gluonts import transform
  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 import Quantile
  from gluonts.model.forecast_generator import QuantileForecastGenerator
  from gluonts.model.predictor import Predictor
  from gluonts.mx.batchify import batchify
  from gluonts.mx.block.decoder import OneShotDecoder
  from gluonts.mx.block.enc2dec import PassThroughEnc2Dec
  from gluonts.mx.block.encoder import HierarchicalCausalConv1DEncoder, MLPEncoder, RNNEncoder, Seq2SeqEncoder
  from gluonts.mx.block.feature import FeatureEmbedder
  from gluonts.mx.block.quantile_output import QuantileOutput
  from gluonts.mx.block.scaler import NOPScaler, Scaler
  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 time_features_from_frequency_str
  from gluonts.transform import ExpectedNumInstanceSampler, InstanceSampler, SelectFields, TestSplitSampler, ValidationSplitSampler
  from ._seq2seq_network import Seq2SeqPredictionNetwork, Seq2SeqTrainingNetwork
label_desc: Use image node_id 1 to create a new CNN2QRForecaster object from inherited base classes: Seq2SeqEstimator with example: obj = CNN2QRForecaster(freq, prediction_length, cardinality, embedding_dimension, decoder_mlp_layer, decoder_mlp_static_dim, scaler, context_length, quantiles, trainer, num_parallel_samples)