class_docstr: Instance splitter used by the Temporal Fusion Transformer model.
  
  Unlike ``InstanceSplitter``, this class returns known dynamic features as
  a single tensor of shape [..., context_length + prediction_length, ...]
  without splitting it into past & future parts. Moreover, this class supports
  dynamic features that are known in the past.
code: def __init__(
      self,
      instance_sampler: InstanceSampler,
      past_length: int,
      future_length: int,
      target_field: str = FieldName.TARGET,
      is_pad_field: str = FieldName.IS_PAD,
      start_field: str = FieldName.START,
      forecast_start_field: str = FieldName.FORECAST_START,
      observed_value_field: str = FieldName.OBSERVED_VALUES,
      lead_time: int = 0,
      output_NTC: bool = True,
      time_series_fields: List[str] = [],
      past_time_series_fields: List[str] = [],
      dummy_value: float = 0.0,
  ) -> None:
      super().__init__(
          target_field=target_field,
          is_pad_field=is_pad_field,
          start_field=start_field,
          forecast_start_field=forecast_start_field,
          instance_sampler=instance_sampler,
          past_length=past_length,
          future_length=future_length,
          lead_time=lead_time,
          output_NTC=output_NTC,
          time_series_fields=time_series_fields,
          dummy_value=dummy_value,
      )
  
      assert past_length > 0, "The value of `past_length` should be > 0"
  
      self.observed_value_field = observed_value_field
      self.past_ts_fields = past_time_series_fields
  
imports: from typing import Iterator, List, Optional, Tuple
  import numpy
  from pandas.tseries.offsets import BaseOffset
  from gluonts.core.component import validated
  from gluonts.dataset.common import DataEntry
  from gluonts.dataset.field_names import FieldName
  from gluonts.zebras._util import pad_axis
  from ._base import FlatMapTransformation
  from .sampler import ContinuousTimePointSampler, InstanceSampler
label_desc: Use image node_id 1 to create a new TFTInstanceSplitter object from inherited base classes: InstanceSplitter with example: obj = TFTInstanceSplitter(instance_sampler, past_length, future_length, target_field, is_pad_field, start_field, forecast_start_field, observed_value_field, lead_time, output_NTC, time_series_fields, past_time_series_fields, dummy_value)