code: def construct_hf_data_dict(hf_x, hf_y, **kwargs):
      hf_data_dict = {
          "x": hf_x,
          "y": hf_y,
          "axis_type": "date"
          if isinstance(hf_x, pd.DatetimeIndex)
          or pd.core.dtypes.common.is_datetime64_any_dtype(hf_x)
          else "linear",
          "downsampler": MinMaxLTTB(),
          "gap_handler": MedDiffGapHandler(),
          "max_n_samples": 1_000,
      }
      hf_data_dict.update(kwargs)
      return hf_data_dict
  
file_path: plotly-resampler/tests/utils.py
imports: from __future__ import annotations
  import sys
  import numpy
  import pandas
  from plotly_resampler.aggregation import MedDiffGapHandler, MinMaxLTTB
  from plotly_resampler.aggregation.aggregation_interface import DataAggregator, DataPointSelector
  from plotly_resampler.aggregation.gap_handler_interface import AbstractGapHandler
  from plotly_resampler.aggregation.plotly_aggregator_parser import PlotlyAggregatorParser
label_desc: Use image node_id 2 for calling a global function with example usage: construct_hf_data_dict(hf_x, hf_y) and returns: hf_data_dict