code: def __init__(
      self,
      input_size: int,
      freq: str,
      prediction_length: int,
      target_dim: int,
      trainer: Trainer = Trainer(),
      context_length: Optional[int] = None,
      num_layers: int = 2,
      num_cells: int = 40,
      cell_type: str = "LSTM",
      num_parallel_samples: int = 100,
      dropout_rate: float = 0.1,
      cardinality: List[int] = [1],
      embedding_dimension: int = 5,
      flow_type="RealNVP",
      n_blocks=3,
      hidden_size=100,
      n_hidden=2,
      conditioning_length: int = 200,
      dequantize: bool = False,
      scaling: bool = True,
      pick_incomplete: bool = False,
      lags_seq: Optional[List[int]] = None,
      time_features: Optional[List[TimeFeature]] = None,
      **kwargs,
  ) -> None:
      super().__init__(trainer=trainer, **kwargs)
  
      self.freq = freq
      self.context_length = (
          context_length
          if context_length is not None
          else prediction_length
      )
  
      self.input_size = input_size
      self.prediction_length = prediction_length
      self.target_dim = target_dim
      self.num_layers = num_layers
      self.num_cells = num_cells
      self.cell_type = cell_type
      self.num_parallel_samples = num_parallel_samples
      self.dropout_rate = dropout_rate
      self.cardinality = cardinality
      self.embedding_dimension = embedding_dimension
  
      self.flow_type = flow_type
      self.n_blocks = n_blocks
      self.hidden_size = hidden_size
      self.n_hidden = n_hidden
      self.conditioning_length = conditioning_length
      self.dequantize = dequantize
  
      self.lags_seq = (
          lags_seq
          if lags_seq is not None
          else lags_for_fourier_time_features_from_frequency(
              freq_str=freq
          )
      )
  
      self.time_features = (
          time_features
          if time_features is not None
          else fourier_time_features_from_frequency(self.freq)
      )
  
      self.history_length = self.context_length + max(self.lags_seq)
      self.pick_incomplete = pick_incomplete
      self.scaling = scaling
  
      self.train_sampler = ExpectedNumInstanceSampler(
          num_instances=1.0,
          min_past=0 if pick_incomplete else self.history_length,
          min_future=prediction_length,
      )
  
      self.validation_sampler = ValidationSplitSampler(
          min_past=0 if pick_incomplete else self.history_length,
          min_future=prediction_length,
      )
  
imports: from typing import List, Optional
  import torch
  from gluonts.core.component import validated
  from gluonts.dataset.field_names import FieldName
  from gluonts.time_feature import TimeFeature
  from gluonts.torch.model.predictor import PyTorchPredictor
  from gluonts.torch.util import copy_parameters
  from gluonts.model.predictor import Predictor
  from gluonts.torch.model.predictor import PyTorchPredictor
  from gluonts.transform import Transformation, Chain, InstanceSplitter, ValidationSplitSampler, TestSplitSampler, ExpectedNumInstanceSampler, RenameFields, AsNumpyArray, ExpandDimArray, AddObservedValuesIndicator, AddTimeFeatures, VstackFeatures, SetFieldIfNotPresent, TargetDimIndicator
  from pts import Trainer
  from pts.feature import fourier_time_features_from_frequency, lags_for_fourier_time_features_from_frequency
  from pts.model.utils import get_module_forward_input_names
  from pts.model import PyTorchEstimator
  from .tempflow_network import TempFlowTrainingNetwork, TempFlowPredictionNetwork
label_desc: Use image node_id 1 to create a new TempFlowEstimator object from inherited base classes: PyTorchEstimator with example: obj = TempFlowEstimator(input_size, freq, prediction_length, target_dim, trainer, context_length, num_layers, num_cells, cell_type, num_parallel_samples, dropout_rate, cardinality, embedding_dimension, flow_type, n_blocks, hidden_size, n_hidden, conditioning_length, dequantize, scaling, pick_incomplete, lags_seq, time_features)