class_docstr: Tests the `create_lagged_training_data` function defined in `darts.utils.data.tabularization`. There are broadly
  two 'groups' of tests defined in this module:
      1. 'Generated Test Cases': these test that `create_lagged_training_data` produces the same outputs
      as a simplified implementation of the 'time intersection' feature generation method (see
      `darts.utils.data.tabularization` for further details). For these tests, the 'correct answer' is not
      directly specified; instead, it is generated from a set of input parameters using the set of simplified
      functions. The rationale behind this approach is that it allows for many different combinations of input
      values to be effortlessly tested. The drawback of this, however, is that the correctness of these tests
      assumes that the simplified functions have been implemented correctly - if this isn't the case, then these
      tests are not to be trusted. In saying this, these simplified functions are significantly easier to
      understand and debug than the `create_lagged_training_data` function they're helping to test.
      2. 'Specified Test Cases': these test that `create_lagged_training_data` returns an exactly specified
      output; these specified outputs are *not* 'generated' by another function. Although these 'specified'
      test cases tend to be simpler and less extensive than the 'generated' test cases, their correctness does
      not assume the correct implementation of any other function.
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docstr: Helper function called by `get_feature_times` that extracts all of the times within a
  `target_series` that can be used to create a feature and label. More specifically,
  we can create features and labels for times within `target_series` that have *both*:
      1. At least `max_lag = -min(lags)` values preceeding them, since these preceeding
      values are required to construct a feature vector for that time. Since the first `max_lag`
      times do not fulfill this condition, they are exluded *if* values from `target_series` are
      to be added to `X`.
      2. At least `(output_chunk_length - 1)` values after them, because the all of the times from
      time `t` to time `t + output_chunk_length - 1` will be used as labels. Since the last
      `(output_chunk_length - 1)` times do not fulfil this condition, they are excluded.
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code: def get_feature_times_target(
      target_series: TimeSeries,
      lags: Optional[Sequence[int]],
      output_chunk_length: int,
  ) -> pd.Index:
      """
      Helper function called by `get_feature_times` that extracts all of the times within a
      `target_series` that can be used to create a feature and label. More specifically,
      we can create features and labels for times within `target_series` that have *both*:
          1. At least `max_lag = -min(lags)` values preceeding them, since these preceeding
          values are required to construct a feature vector for that time. Since the first `max_lag`
          times do not fulfill this condition, they are exluded *if* values from `target_series` are
          to be added to `X`.
          2. At least `(output_chunk_length - 1)` values after them, because the all of the times from
          time `t` to time `t + output_chunk_length - 1` will be used as labels. Since the last
          `(output_chunk_length - 1)` times do not fulfil this condition, they are excluded.
      """
      times = target_series.time_index
      if lags is not None:
          max_lag = -min(lags)
          times = times[max_lag:]
      if output_chunk_length > 1:
          times = times[: -output_chunk_length + 1]
      return times
  
imports: import warnings
  from itertools import product
  from typing import Optional, Sequence
  import numpy
  import pandas
  import pytest
  from darts import TimeSeries
  from darts import concatenate
  from darts.logging import get_logger, raise_if, raise_if_not, raise_log
  from darts.utils.data.tabularization import create_lagged_component_names, create_lagged_training_data
  from darts.utils.timeseries_generation import linear_timeseries
label_desc: Use image node_id 3 for calling the TestCreateLaggedTrainingData obj's underlying member method code with example usage: obj.get_feature_times_target(target_series, lags, output_chunk_length) and returns: times