code: def __init__(
      self,
      input_size: int,
      freq: str,
      prediction_length: int,
      context_length: Optional[int] = None,
      trainer: Trainer = Trainer(),
      dropout_rate: float = 0.1,
      cardinality: Optional[List[int]] = None,
      embedding_dimension: List[int] = [20],
      distr_output: DistributionOutput = StudentTOutput(),
      d_model: int = 32,
      dim_feedforward_scale: int = 4,
      act_type: str = "gelu",
      num_heads: int = 8,
      num_encoder_layers: int = 3,
      num_decoder_layers: int = 3,
      scaling: bool = True,
      lags_seq: Optional[List[int]] = None,
      time_features: Optional[List[TimeFeature]] = None,
      use_feat_dynamic_real: bool = False,
      use_feat_static_cat: bool = False,
      use_feat_static_real: bool = False,
      num_parallel_samples: int = 100,
  ) -> None:
      super().__init__(trainer=trainer)
  
      self.input_size = input_size
      self.freq = freq
      self.prediction_length = prediction_length
      self.context_length = (
          context_length
          if context_length is not None
          else prediction_length
      )
      self.distr_output = distr_output
      self.dropout_rate = dropout_rate
      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.cardinality = cardinality if use_feat_static_cat else [1]
      self.embedding_dimension = embedding_dimension
      self.num_parallel_samples = num_parallel_samples
      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.scaling = scaling
  
      self.d_model = d_model
      self.num_heads = num_heads
      self.act_type = act_type
      self.dim_feedforward_scale = dim_feedforward_scale
      self.num_encoder_layers = num_encoder_layers
      self.num_decoder_layers = num_decoder_layers
  
      self.train_sampler = ExpectedNumInstanceSampler(
          num_instances=1.0, min_future=prediction_length
      )
      self.validation_sampler = ValidationSplitSampler(
          min_future=prediction_length
      )
  
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.time_feature import TimeFeature
  from gluonts.torch.modules.distribution_output import DistributionOutput
  from gluonts.torch.util import copy_parameters
  from gluonts.torch.model.predictor import PyTorchPredictor
  from gluonts.model.predictor import Predictor
  from gluonts.transform import Transformation, Chain, InstanceSplitter, InstanceSampler, ValidationSplitSampler, TestSplitSampler, ExpectedNumInstanceSampler, RemoveFields, AddAgeFeature, AsNumpyArray, AddObservedValuesIndicator, AddTimeFeatures, VstackFeatures, SetField
  from pts import Trainer
  from pts.model.utils import get_module_forward_input_names
  from pts.feature import fourier_time_features_from_frequency, lags_for_fourier_time_features_from_frequency
  from pts.model import PyTorchEstimator
  from pts.modules import StudentTOutput
  from .transformer_network import TransformerTrainingNetwork, TransformerPredictionNetwork
label_desc: Use image node_id 1 to create a new TransformerEstimator object from inherited base classes: PyTorchEstimator with example: obj = TransformerEstimator(input_size, freq, prediction_length, context_length, trainer, dropout_rate, cardinality, embedding_dimension, distr_output, d_model, dim_feedforward_scale, act_type, num_heads, num_encoder_layers, num_decoder_layers, scaling, lags_seq, time_features, use_feat_dynamic_real, use_feat_static_cat, use_feat_static_real, num_parallel_samples)