code: def populated_column_map(self):
          '''Return the _column_map without unused optional fields'''
          column_map = []
          cls = self.model
          for csv_name, field_pattern in cls._column_map:
              # Separate the local field name from foreign columns
              if '__' in field_pattern:
                  field_name = field_pattern.split('__', 1)[0]
              else:
                  field_name = field_pattern
  
              # Handle point fields
              point_match = re_point.match(field_name)
              if point_match:
                  field = None
              else:
                  field = cls._meta.get_field(field_name)
  
              # Only add optional columns if they are used in the records
              if field and field.blank and not field.has_default():
                  kwargs = {field_name: get_blank_value(field)}
                  if self.exclude(**kwargs).exists():
                      column_map.append((csv_name, field_pattern))
              else:
                  column_map.append((csv_name, field_pattern))
          return column_maf populated_column_map(self):
          '''Return the _column_map without unused optional fields'''
          column_map = []
          cls = self.model
          for csv_name, field_pattern in cls._column_map:
              # Separate the local field name from foreign columns
              if '__' in field_pattern:
                  field_name = field_pattern.split('__', 1)[0]
              else:
                  field_name = field_pattern
  
              # Handle point fields
              point_match = re_point.match(field_name)
              if point_match:
                  field = None
              else:
                  field = cls._meta.get_field(field_name)
  
              # Only add optional columns if they are used in the records
              if field and field.blank and not field.has_default():
                  kwargs = {field_name: get_blank_value(field)}
                  if self.exclude(**kwargs).exists():
                      column_map.append((csv_name, field_pattern))
              else:
                  column_map.append((csv_name, field_pattern))
          return column_map
docs: Return the _column_map without unused optional fields