code: def get_dummies(
      data,
      prefix=None,
      prefix_sep="_",
      dummy_na=False,
      columns=None,
      sparse=False,
      drop_first=False,
      dtype=None,
  ):
      
      if sparse:
          raise NotImplementedError(
              "SparseDataFrame is not implemented. "
              "To contribute to Modin, please visit "
              "github.com/modin-project/modin."
          )
      if not isinstance(data, DataFrame):
          ErrorMessage.default_to_pandas("`get_dummies` on non-DataFrame")
          return DataFrame(
              pandas.get_dummies(
                  data,
                  prefix=prefix,
                  prefix_sep=prefix_sep,
                  dummy_na=dummy_na,
                  columns=columns,
                  sparse=sparse,
                  drop_first=drop_first,
                  dtype=dtype,
              )
          )
      else:
          new_manager = data._query_compiler.get_dummies(
              columns,
              prefix=prefix,
              prefix_sep=prefix_sep,
              dummy_na=dummy_na,
              drop_first=drop_first,
              dtype=dtype,
          )
          return DataFrame(query_compiler=new_manager)
docs: Convert categorical variable into indicator variables.
  
      Args:
          data (array-like, Series, or DataFrame): data to encode.
          prefix (string, [string]): Prefix to apply to each encoded column
                                     label.
          prefix_sep (string, [string]): Separator between prefix and value.
          dummy_na (bool): Add a column to indicate NaNs.
          columns: Which columns to encode.
          sparse (bool): Not Implemented: If True, returns SparseDataFrame.
          drop_first (bool): Whether to remove the first level of encoded data.
          dtype: The dtype for the get_dummies call.
  
      Returns:
          DataFrame or one-hot encoded data.