code: def __init__(
      self,
      freq: str,
      prediction_length: Optional[int],
      context_length: int,
      num_series: int,
      ar_window: int = 24,
      skip_size: int = 24,
      channels: int = 100,
      kernel_size: int = 6,
      horizon: Optional[int] = None,
      trainer: Trainer = Trainer(),
      dropout_rate: Optional[float] = 0.2,
      output_activation: Optional[str] = None,
      rnn_cell_type: str = "GRU",
      rnn_num_cells: int = 100,
      skip_rnn_cell_type: str = "GRU",
      skip_rnn_num_cells: int = 5,
      scaling: bool = True,
      dtype: np.dtype = np.float32,
  ):
      super().__init__(trainer, dtype=dtype)
  
      self.freq = freq
      self.num_series = num_series
      self.skip_size = skip_size
      self.ar_window = ar_window
      self.horizon = horizon
      self.prediction_length = prediction_length
  
      self.future_length = (
          horizon if horizon is not None else prediction_length
      )
      self.context_length = context_length
      self.channels = channels
      self.kernel_size = kernel_size
      self.dropout_rate = dropout_rate
      self.output_activation = output_activation
      self.rnn_cell_type = rnn_cell_type
      self.rnn_num_cells = rnn_num_cells
      self.skip_rnn_cell_type = skip_rnn_cell_type
      self.skip_rnn_num_cells = skip_rnn_num_cells
      self.scaling = scaling
  
      self.train_sampler = ExpectedNumInstanceSampler(
          num_instances=1.0, min_future=self.future_length
      )
      self.validation_sampler = ValidationSplitSampler(
          min_future=self.future_length
      )
  
      self.dtype = dtype
  
imports: from typing import List, Optional
  import numpy
  import torch
  import torch.nn
  from gluonts.core.component import validated
  from gluonts.dataset.field_names import FieldName
  from gluonts.torch.util import copy_parameters
  from gluonts.torch.model.predictor import PyTorchPredictor
  from gluonts.model.predictor import Predictor
  from gluonts.transform import InstanceSplitter, ValidationSplitSampler, TestSplitSampler, Transformation, Chain, ExpectedNumInstanceSampler, AddObservedValuesIndicator, AsNumpyArray
  from pts.model import PyTorchEstimator
  from pts import Trainer
  from pts.model.utils import get_module_forward_input_names
  from .lstnet_network import LSTNetTrain, LSTNetPredict
label_desc: Use image node_id 1 to create a new LSTNetEstimator object from inherited base classes: PyTorchEstimator with example: obj = LSTNetEstimator(freq, prediction_length, context_length, num_series, ar_window, skip_size, channels, kernel_size, horizon, trainer, dropout_rate, output_activation, rnn_cell_type, rnn_num_cells, skip_rnn_cell_type, skip_rnn_num_cells, scaling, dtype)