code: def _convert_predict_column_into_desired_type(
      current_column: pd.Series, types_sr: pd.Series, log: LoggerAdapter
  ):
      current_type = types_sr.loc[current_column.name]
      try:
          converted_column = current_column.astype(current_type)
          if current_type is str:
              has_comma_and_dot = np.isin(
                  [".", ","], current_column
              ).all()
              if has_comma_and_dot:
                  # Most likely case: '20,000' must be converted into '20.000'
                  warning = f'Column {current_column.name} contains both "." and ",". Standardize it.'
                  log.warning(warning)
      except ValueError:
          converted_column = current_column.apply(
              _process_predict_column_values_one_by_one,
              current_type=current_type,
          )
      return converted_column
  
imports: from __future__ import annotations
  from collections.abc import Sequence
  from typing import TYPE_CHECKING, Dict, List, Optional, Tuple
  import numpy
  import pandas
  from golem.core.log import LoggerAdapter, default_log
  from fedot.core.repository.tasks import Task, TaskTypesEnum
label_desc: Use image node_id 7 for calling a global function with example usage: _convert_predict_column_into_desired_type(current_column, types_sr, log) and returns: converted_column