class_docstr: Adds an 'age' feature to the data_entry.
  
  The age feature starts with a small value at the start of the time series
  and grows over time.
  
  If `is_train=True` the age feature has the same length as the `target`
  field.
  If `is_train=False` the age feature has length len(target) + pred_length
  
  Parameters
  ----------
  target_field
      Field with target values (array) of time series
  output_field
      Field name to use for the output.
  pred_length
      Prediction length
  log_scale
      If set to true the age feature grows logarithmically otherwise linearly
      over time.
class_docstr_tok: ["Adds","an","'age","'","feature","to","the","data_entry",".","The","age","feature","starts","with","a","small","value","at","the","start","of","the","time","series","and","grows","over","time",".","If","`","is_train=True","`","the","age","feature","has","the","same","length","as","the","`","target","`","field",".","If","`","is_train=False","`","the","age","feature","has","length","len","(","target",")","+","pred_length","Parameters","--","--","--","--","--","target_field","Field","with","target","values","(","array",")","of","time","series","output_field","Field","name","to","use","for","the","output",".","pred_length","Prediction","length","log_scale","If","set","to","true","the","age","feature","grows","logarithmically","otherwise","linearly","over","time","."]
code: def __init__(
      self,
      target_field: str,
      output_field: str,
      pred_length: int,
      log_scale: bool = True,
      dtype: Type = np.float32,
  ) -> None:
      self.pred_length = pred_length
      self.target_field = target_field
      self.feature_name = output_field
      self.log_scale = log_scale
      self._age_feature = np.zeros(0)
      self.dtype = dtype
  
imports: from typing import List, Optional, Union, Type
  import numpy
  import pandas
  from gluonts.core.component import validated
  from gluonts.dataset.common import DataEntry
  from gluonts.dataset.field_names import FieldName
  from gluonts.time_feature import TimeFeature
  from ._base import MapTransformation, SimpleTransformation
label_desc: Use image node_id 1 to create a new AddAgeFeature object from inherited base classes: MapTransformation with example: obj = AddAgeFeature(target_field, output_field, pred_length, log_scale, dtype)