code: def get_dummies(
      df,
      prefix=None,
      prefix_sep="_",
      dummy_na=False,
      columns=None,
      cats=None,
      sparse=False,
      drop_first=False,
      dtype=no_default,
  ):
      """Returns a dataframe whose columns are the one hot encodings of all
      columns in `df`
  
      Parameters
      ----------
      df : array-like, Series, or DataFrame
          Data of which to get dummy indicators.
      prefix : str, dict, or sequence, optional
          Prefix to append. Either a str (to apply a constant prefix), dict
          mapping column names to prefixes, or sequence of prefixes to apply with
          the same length as the number of columns. If not supplied, defaults
          to the empty string
      prefix_sep : str, dict, or sequence, optional, default '_'
          Separator to use when appending prefixes
      dummy_na : boolean, optional
          Add a column to indicate Nones, if False Nones are ignored.
      cats : dict, optional
          Dictionary mapping column names to sequences of values representing
          that column's category. If not supplied, it is computed as the unique
          values of the column.
      sparse : boolean, optional
          Right now this is NON-FUNCTIONAL argument in rapids.
      drop_first : boolean, optional
          Right now this is NON-FUNCTIONAL argument in rapids.
      columns : sequence of str, optional
          Names of columns to encode. If not provided, will attempt to encode all
          columns. Note this is different from pandas default behavior, which
          encodes all columns with dtype object or categorical
      dtype : str, optional
          Output dtype, default 'uint8'
  
      Examples
      --------
      >>> import cudf
      >>> df = cudf.DataFrame({"a": ["value1", "value2", None], "b": [0, 0, 0]})
      >>> cudf.get_dummies(df)
         b  a_value1  a_value2
      0  0         1         0
      1  0         0         1
      2  0         0         0
  
      >>> cudf.get_dummies(df, dummy_na=True)
         b  a_None  a_value1  a_value2
      0  0       0         1         0
      1  0       0         0         1
      2  0       1         0         0
  
      >>> import numpy as np
      >>> df = cudf.DataFrame({"a":cudf.Series([1, 2, np.nan, None],
      ...                     nan_as_null=False)})
      >>> df
            a
      0   1.0
      1   2.0
      2   NaN
      3  <NA>
  
      >>> cudf.get_dummies(df, dummy_na=True, columns=["a"])
         a_1.0  a_2.0  a_nan  a_null
      0      1      0      0       0
      1      0      1      0       0
      2      0      0      1       0
      3      0      0      0       1
  
      >>> series = cudf.Series([1, 2, None, 2, 4])
      >>> series
      0       1
      1       2
      2    <NA>
      3       2
      4       4
      dtype: int64
      >>> cudf.get_dummies(series, dummy_na=True)
         null  1  2  4
      0     0  1  0  0
      1     0  0  1  0
      2     1  0  0  0
      3     0  0  1  0
      4     0  0  0  1
      """
  
      if cats is None:
          cats = {}
      if sparse:
          raise NotImplementedError("sparse is not supported yet")
  
      if drop_first:
          raise NotImplementedError("drop_first is not supported yet")
  
      if dtype is no_default:
          # Do not remove until pandas 2.0 support is added.
          warnings.warn(
              "Default `dtype` value will be changed to 'bool' in a future "
              "release, please update `dtype='bool'` to adapt for "
              "future behavior.",
              FutureWarning,
          )
          dtype = "uint8"
  
      if isinstance(df, cudf.DataFrame):
          encode_fallback_dtypes = ["object", "category"]
  
          if columns is None or len(columns) == 0:
              columns = df.select_dtypes(
                  include=encode_fallback_dtypes
              )._column_names
  
          _length_check_params(prefix, columns, "prefix")
          _length_check_params(prefix_sep, columns, "prefix_sep")
  
          if prefix is None:
              prefix = columns
  
          if isinstance(prefix, str):
              prefix_map = {}
          elif isinstance(prefix, dict):
              prefix_map = prefix
          else:
              prefix_map = dict(zip(columns, prefix))
  
          if isinstance(prefix_sep, str):
              prefix_sep_map = {}
          elif isinstance(prefix_sep, dict):
              prefix_sep_map = prefix_sep
          else:
              prefix_sep_map = dict(zip(columns, prefix_sep))
  
          # If we have no columns to encode, we need to drop
          # fallback columns(if any)
          if len(columns) == 0:
              return df.select_dtypes(exclude=encode_fallback_dtypes)
          else:
              result_data = {
                  col_name: col
                  for col_name, col in df._data.items()
                  if col_name not in columns
              }
  
              for name in columns:
                  if name not in cats:
                      unique = _get_unique(
                          column=df._data[name], dummy_na=dummy_na
                      )
                  else:
                      unique = as_column(cats[name])
  
                  col_enc_data = _one_hot_encode_column(
                      column=df._data[name],
                      categories=unique,
                      prefix=prefix_map.get(name, prefix),
                      prefix_sep=prefix_sep_map.get(name, prefix_sep),
                      dtype=dtype,
                  )
                  result_data.update(col_enc_data)
              return cudf.DataFrame._from_data(
                  result_data, index=df._index
              )
      else:
          ser = cudf.Series(df)
          unique = _get_unique(column=ser._column, dummy_na=dummy_na)
          data = _one_hot_encode_column(
              column=ser._column,
              categories=unique,
              prefix=prefix,
              prefix_sep=prefix_sep,
              dtype=dtype,
          )
          return cudf.DataFrame._from_data(data, index=ser._index)
  
imports: import itertools
  import warnings
  from collections import abc
  from typing import Dict, Optional
  import cupy
  import numpy
  import pandas
  import cudf
  from cudf._lib.transform import one_hot_encode
  from cudf._lib.types import size_type_dtype
  from cudf._typing import Dtype
  from cudf.api.extensions import no_default
  from cudf.core.column import ColumnBase, as_column, column_empty_like
  from cudf.core.column.categorical import CategoricalColumn
  from cudf.utils.dtypes import min_unsigned_type
label_desc: Use image node_id 6 for calling a global function with example usage: get_dummies(df, prefix, prefix_sep, dummy_na, columns, cats, sparse, drop_first, dtype) and returns: cudf, df, cudf