code: def lift_over_coordinates(
      contigName: Union[Column, str],
      start: Union[Column, str],
      end: Union[Column, str],
      chainFile: str,
      minMatchRatio: float = None,
  ) -> Column:
      """
      Performs liftover for the coordinates of a variant. To perform liftover of alleles and add additional metadata, see :ref:`liftover`.
  
      Added in version 0.3.0.
  
      Examples:
          >>> df = spark.read.format('vcf').load('test-data/liftover/unlifted.test.vcf').where('start = 18210071')
          >>> chain_file = 'test-data/liftover/hg38ToHg19.over.chain.gz'
          >>> reference_file = 'test-data/liftover/hg19.chr20.fa.gz'
          >>> df.select('contigName', 'start', 'end').head()
          Row(contigName='chr20', start=18210071, end=18210072)
          >>> lifted_df = df.select(glow.expand_struct(glow.lift_over_coordinates('contigName', 'start', 'end', chain_file)))
          >>> lifted_df.head()
          Row(contigName='chr20', start=18190715, end=18190716)
  
      Args:
          contigName : The current contig name
          start : The current start
          end : The current end
          chainFile : Location of the chain file on each node in the cluster
          minMatchRatio : Minimum fraction of bases that must remap to do liftover successfully. If not provided, defaults to ``0.95``.
  
      Returns:
          A struct containing ``contigName``, ``start``, and ``end`` fields after liftover
      """
      assert check_argument_types()
      if minMatchRatio is None:
          output = Column(
              sc()._jvm.io.projectglow.functions.lift_over_coordinates(
                  _to_java_column(contigName),
                  _to_java_column(start),
                  _to_java_column(end),
                  chainFile,
              )
          )
      else:
          output = Column(
              sc()._jvm.io.projectglow.functions.lift_over_coordinates(
                  _to_java_column(contigName),
                  _to_java_column(start),
                  _to_java_column(end),
                  chainFile,
                  minMatchRatio,
              )
          )
      assert check_return_type(output)
      return output
  
imports: from pyspark import SparkContext
  from pyspark.sql.column import Column, _to_java_column, _to_seq
  from typeguard import check_argument_types, check_return_type
  from typing import Union
label_desc: Use image node_id 11 for calling a global function with example usage: lift_over_coordinates(contigName, start, end, chainFile, minMatchRatio) and returns: output