code: def test_sparse_matrix():
      # Create lagged matrix for sparse
      train_input, _, _ = synthetic_univariate_ts()
      _, lagged_table = ts_to_table(
          idx=train_input.idx,
          time_series=train_input.features,
          window_size=_WINDOW_SIZE,
      )
      features_columns = _sparse_matrix(
          default_log(prefix=__name__), lagged_table
      )
  
      # assert if sparse matrix features less than half or less than another dimension
      assert features_columns.shape[0] == lagged_table.shape[0]
      assert (
          features_columns.shape[1] <= lagged_table.shape[1] / 2
          or features_columns.shape[1] < lagged_table.shape[0]
      )
  
imports: import logging
  from copy import deepcopy
  import numpy
  import pytest
  from golem.core.log import default_log
  from fedot.core.data.data import InputData
  from fedot.core.data.data_split import train_test_data_setup
  from fedot.core.data.multi_modal import MultiModalData
  from fedot.core.operations.evaluation.operation_implementations.data_operations.ts_transformations import _sparse_matrix, prepare_target, ts_to_table
  from fedot.core.optimisers.objective import MetricsObjective, PipelineObjectiveEvaluate
  from fedot.core.optimisers.objective.data_source_splitter import DataSourceSplitter
  from fedot.core.pipelines.node import PipelineNode
  from fedot.core.pipelines.pipeline import Pipeline
  from fedot.core.pipelines.pipeline_builder import PipelineBuilder
  from fedot.core.pipelines.tuning.tuner_builder import TunerBuilder
  from fedot.core.repository.dataset_types import DataTypesEnum
  from fedot.core.repository.tasks import Task, TaskTypesEnum, TsForecastingParams
label_desc: Use image node_id 7 for calling a global function with example usage: test_sparse_matrix() without return types