code: def ts_fit(
      series: TimeSeries, params: Mapping[str, Any], **kwargs
  ) -> Any:
      lags, dropna = (
          params["fixed"]["_lags"],
          params["fixed"]["_dropna"],
      )
      lags_sum = sum(lags)
      raise_if(
          series.n_timesteps <= lags_sum,
          (
              f"Series requires at least {lags_sum + 1} timesteps "
              f"to difference with lags {lags}; series only has "
              f"{series.n_timesteps} timesteps."
          ),
          logger,
      )
      component_mask = Diff._get_component_mask(kwargs, dropna)
      vals = Diff.apply_component_mask(
          series, component_mask, return_ts=False
      )
      # First `lags_sum` values of time series will be 'lost' due to differencing;
      # need to remember these values to 'undifference':
      start_vals = vals[:lags_sum, :, :]
      diffed = start_vals
      cutoff = 0
      for lag in lags:
          # Store first `lag` values of current differencing step:
          start_vals[cutoff:, :, :] = diffed
          diffed = diffed[lag:, :, :] - diffed[:-lag, :, :]
          cutoff += lag
      return (
          start_vals,
          component_mask,
          series.start_time(),
          series.freq,
      )
  
file_path: darts/darts/dataprocessing/transformers/diff.py
imports: from typing import Any, Mapping, Sequence, Union
  import numpy
  from darts.logging import get_logger, raise_if, raise_if_not
  from darts.timeseries import TimeSeries
  from .fittable_data_transformer import FittableDataTransformer
  from .invertible_data_transformer import InvertibleDataTransformer
label_desc: Use image node_id 2 for calling the Diff obj's underlying member method code with example usage: obj.ts_fit(series, params) and returns: start_vals,  component_mask,  series,  series