code: def get_dummies(
      data: Union[DataFrame, Series],
      prefix: Optional[Union[str, List[str], Dict[str, str]]] = None,
      prefix_sep: str = "_",
      dummy_na: bool = False,
      columns: Optional[Union[Name, List[Name]]] = None,
      sparse: bool = False,
      drop_first: bool = False,
      dtype: Optional[Union[str, Dtype]] = None,
  ) -> DataFrame:
      """
      Convert categorical variable into dummy/indicator variables, also
      known as one hot encoding.
  
      Parameters
      ----------
      data : array-like, Series, or DataFrame
      prefix : string, list of strings, or dict of strings, default None
          String to append DataFrame column names.
          Pass a list with length equal to the number of columns
          when calling get_dummies on a DataFrame. Alternatively, `prefix`
          can be a dictionary mapping column names to prefixes.
      prefix_sep : string, default '_'
          If appending prefix, separator/delimiter to use. Or pass a
          list or dictionary as with `prefix.`
      dummy_na : bool, default False
          Add a column to indicate NaNs, if False NaNs are ignored.
      columns : list-like, default None
          Column names in the DataFrame to be encoded.
          If `columns` is None then all the columns with
          `object` or `category` dtype will be converted.
      sparse : bool, default False
          Whether the dummy-encoded columns should be be backed by
          a :class:`SparseArray` (True) or a regular NumPy array (False).
          In pandas-on-Spark, this value must be "False".
      drop_first : bool, default False
          Whether to get k-1 dummies out of k categorical levels by removing the
          first level.
      dtype : dtype, default np.uint8
          Data type for new columns. Only a single dtype is allowed.
  
      Returns
      -------
      dummies : DataFrame
  
      See Also
      --------
      Series.str.get_dummies
  
      Examples
      --------
      >>> s = ps.Series(list('abca'))
  
      >>> ps.get_dummies(s)
         a  b  c
      0  1  0  0
      1  0  1  0
      2  0  0  1
      3  1  0  0
  
      >>> df = ps.DataFrame({'A': ['a', 'b', 'a'], 'B': ['b', 'a', 'c'],
      ...                    'C': [1, 2, 3]},
      ...                   columns=['A', 'B', 'C'])
  
      >>> ps.get_dummies(df, prefix=['col1', 'col2'])
         C  col1_a  col1_b  col2_a  col2_b  col2_c
      0  1       1       0       0       1       0
      1  2       0       1       1       0       0
      2  3       1       0       0       0       1
  
      >>> ps.get_dummies(ps.Series(list('abcaa')))
         a  b  c
      0  1  0  0
      1  0  1  0
      2  0  0  1
      3  1  0  0
      4  1  0  0
  
      >>> ps.get_dummies(ps.Series(list('abcaa')), drop_first=True)
         b  c
      0  0  0
      1  1  0
      2  0  1
      3  0  0
      4  0  0
  
      >>> ps.get_dummies(ps.Series(list('abc')), dtype=float)
           a    b    c
      0  1.0  0.0  0.0
      1  0.0  1.0  0.0
      2  0.0  0.0  1.0
      """
      if sparse is not False:
          raise NotImplementedError(
              "get_dummies currently does not support sparse"
          )
  
      if columns is not None and not is_list_like(columns):
          raise TypeError(
              "Input must be a list-like for parameter `columns`"
          )
  
      if dtype is None:
          dtype = "byte"
  
      if isinstance(data, Series):
          if prefix is not None:
              prefix = [str(prefix)]
          psdf = data.to_frame()
          column_labels = psdf._internal.column_labels
          remaining_columns = []
      else:
          if isinstance(prefix, str):
              raise NotImplementedError(
                  "get_dummies currently does not support prefix as string types"
              )
          psdf = data.copy()
  
          if columns is None:
              column_labels = [
                  label
                  for label in psdf._internal.column_labels
                  if isinstance(
                      psdf._internal.spark_type_for(label),
                      _get_dummies_default_accept_types,
                  )
              ]
          else:
              if is_name_like_tuple(columns):
                  column_labels = [
                      label
                      for label in psdf._internal.column_labels
                      if label[: len(columns)] == columns
                  ]
                  if len(column_labels) == 0:
                      raise KeyError(name_like_string(columns))
                  if prefix is None:
                      prefix = [
                          str(label[len(columns) :])
                          if len(label) > len(columns) + 1
                          else label[len(columns)]
                          if len(label) == len(columns) + 1
                          else ""
                          for label in column_labels
                      ]
              elif any(
                  isinstance(col, tuple) for col in columns
              ) and any(not is_name_like_tuple(col) for col in columns):
                  raise ValueError(
                      "Expected tuple, got {}".format(
                          type(
                              set(
                                  col
                                  for col in columns
                                  if not is_name_like_tuple(col)
                              ).pop()
                          )
                      )
                  )
              else:
                  column_labels = [
                      label
                      for key in columns
                      for label in psdf._internal.column_labels
                      if label == key or label[0] == key
                  ]
          if len(column_labels) == 0:
              if columns is None:
                  return psdf
              raise KeyError("{} not in index".format(columns))
  
          if prefix is None:
              prefix = [
                  str(label) if len(label) > 1 else label[0]
                  for label in column_labels
              ]
  
          column_labels_set = set(column_labels)
          remaining_columns = [
              (
                  psdf[label]
                  if psdf._internal.column_labels_level == 1
                  else psdf[label].rename(name_like_string(label))
              )
              for label in psdf._internal.column_labels
              if label not in column_labels_set
          ]
  
      if any(
          not isinstance(
              psdf._internal.spark_type_for(label),
              _get_dummies_acceptable_types,
          )
          for label in column_labels
      ):
          raise NotImplementedError(
              "get_dummies currently only accept {} values".format(
                  ", ".join(
                      [
                          cast(Type[DataType], t).typeName()
                          for t in _get_dummies_acceptable_types
                      ]
                  )
              )
          )
  
      if prefix is not None and len(column_labels) != len(prefix):
          raise ValueError(
              "Length of 'prefix' ({}) did not match the length of "
              "the columns being encoded ({}).".format(
                  len(prefix), len(column_labels)
              )
          )
      elif isinstance(prefix, dict):
          prefix = [
              prefix[column_label[0]] for column_label in column_labels
          ]
  
      all_values = _reduce_spark_multi(
          psdf._internal.spark_frame,
          [
              F.collect_set(psdf._internal.spark_column_for(label))
              for label in column_labels
          ],
      )
      for i, label in enumerate(column_labels):
          values = all_values[i]
          if isinstance(values, np.ndarray):
              values = values.tolist()
          values = sorted(values)
          if drop_first:
              values = values[1:]
  
          def column_name(v: Any) -> Name:
              if prefix is None or cast(List[str], prefix)[i] == "":
                  return v
              else:
                  return "{}{}{}".format(
                      cast(List[str], prefix)[i], prefix_sep, v
                  )
  
          for value in values:
              remaining_columns.append(
                  (psdf[label].notnull() & (psdf[label] == value))
                  .astype(dtype)
                  .rename(column_name(value))
              )
          if dummy_na:
              remaining_columns.append(
                  psdf[label]
                  .isnull()
                  .astype(dtype)
                  .rename(column_name(np.nan))
              )
  
      return psdf[remaining_columns]
  
imports: from typing import Any, Callable, Dict, List, Optional, Set, Sized, Tuple, Type, Union, cast, no_type_check
  from collections.abc import Iterable
  from datetime import tzinfo
  from functools import reduce
  from io import BytesIO
  import json
  import warnings
  import numpy
  import pandas
  from pandas.api.types import is_datetime64_dtype, is_list_like
  from pandas.tseries.offsets import DateOffset
  import pyarrow
  import pyarrow.parquet
  from pyspark.sql import functions, Column
  from pyspark.sql.functions import pandas_udf
  from pyspark.sql.types import ByteType, ShortType, IntegerType, LongType, FloatType, DoubleType, BooleanType, TimestampType, TimestampNTZType, DecimalType, StringType, DateType, StructType, DataType
  from pyspark.sql.dataframe import DataFrame
  from pyspark import pandas
  from pyspark.pandas._typing import Axis, Dtype, Label, Name
  from pyspark.pandas.base import IndexOpsMixin
  from pyspark.pandas.utils import align_diff_frames, default_session, is_name_like_tuple, is_name_like_value, name_like_string, same_anchor, scol_for, validate_axis, log_advice
  from pyspark.pandas.frame import DataFrame, _reduce_spark_multi
  from pyspark.pandas.internal import InternalFrame, DEFAULT_SERIES_NAME, HIDDEN_COLUMNS, SPARK_INDEX_NAME_FORMAT
  from pyspark.pandas.series import Series, first_series
  from pyspark.pandas.spark.utils import as_nullable_spark_type, force_decimal_precision_scale
  from pyspark.pandas.indexes import Index, DatetimeIndex, TimedeltaIndex
  from pyspark.pandas.indexes.multi import MultiIndex
  from pyspark.sql.utils import get_column_class
label_desc: Use image node_id 19 for calling a global function with example usage: get_dummies(data, prefix, prefix_sep, dummy_na, columns, sparse, drop_first, dtype) and returns: psdf, psdf, v, str