code: def __init__(
      self,
      input_feature_config: TimeseriesInputFeatureConfig,
      encoder_obj=None,
      **kwargs,
  ):
      # add required sequence encoder parameters for time series
      input_feature_config.encoder.embedding_size = 1
      input_feature_config.encoder.should_embed = False
  
      # SequenceInputFeauture's constructor initializes the encoder.
      super().__init__(
          input_feature_config, encoder_obj=encoder_obj, **kwargs
      )
  
imports: import logging
  from typing import Dict, List, TYPE_CHECKING, Union
  import numpy
  import torch
  from ludwig.constants import COLUMN, HIDDEN, LOGITS, NAME, PREDICTIONS, PROC_COLUMN, TIMESERIES
  from ludwig.features.base_feature import BaseFeatureMixin, OutputFeature, PredictModule
  from ludwig.features.sequence_feature import SequenceInputFeature
  from ludwig.features.vector_feature import _VectorPostprocessing, _VectorPredict
  from ludwig.schema.features.timeseries_feature import TimeseriesInputFeatureConfig, TimeseriesOutputFeatureConfig
  from ludwig.types import FeatureMetadataDict, ModelConfigDict, PreprocessingConfigDict, TrainingSetMetadataDict
  from ludwig.utils.tokenizers import get_tokenizer_from_registry, TORCHSCRIPT_COMPATIBLE_TOKENIZERS
  from ludwig.utils.types import Series, TorchscriptPreprocessingInput
label_desc: Use image node_id 1 to create a new TimeseriesInputFeature object from inherited base classes: TimeseriesFeatureMixin, SequenceInputFeature with example: obj = TimeseriesInputFeature(input_feature_config, encoder_obj)