code: def get_dataframe(parameters, use_threads):
      # Initialize seeds
      if parameters.seed is not None:
          np.random.seed(parameters.seed)
  
      # For each column, invoke the data generator
      for column_params in parameters.column_parameters:
          column_params.generator = column_params.generator()
  
      # Get schema for each column
      table_fields = []
      for i, column_params in enumerate(parameters.column_parameters):
          if (
              isinstance(column_params.dtype, str)
              and column_params.dtype == "category"
          ):
              arrow_type = pa.dictionary(
                  index_type=pa.int64(),
                  value_type=np_to_pa_dtype(
                      cudf.dtype(
                          type(next(iter(column_params.generator)))
                      )
                  ),
              )
          elif hasattr(column_params.dtype, "to_arrow"):
              arrow_type = column_params.dtype.to_arrow()
          else:
              arrow_type = np_to_pa_dtype(
                  cudf.dtype(type(next(iter(column_params.generator))))
                  if column_params.dtype is None
                  else column_params.dtype
              )
          table_fields.append(
              pa.field(
                  name=str(i),
                  type=arrow_type,
                  nullable=column_params.null_frequency > 0,
              )
          )
  
      schema = pa.schema(table_fields)
  
      # Initialize column data and which columns should be sorted
      column_data = [None] * len(parameters.column_parameters)
      columns_to_sort = [
          str(i)
          for i, column_params in enumerate(
              parameters.column_parameters
          )
          if column_params.is_sorted
      ]
      # Generate data
      if not use_threads:
          for i, column_params in enumerate(
              parameters.column_parameters
          ):
              column_data[i] = _generate_column(
                  column_params, parameters.num_rows
              )
      else:
          pool = Pool(pa.cpu_count())
          column_data = pool.starmap(
              _generate_column,
              [
                  (column_params, parameters.num_rows)
                  for i, column_params in enumerate(
                      parameters.column_parameters
                  )
              ],
          )
          pool.close()
          pool.join()
      # Convert to Pandas DataFrame and sort columns appropriately
      tbl = pa.Table.from_arrays(
          column_data,
          schema=schema,
      )
      if columns_to_sort:
          tbl = tbl.to_pandas()
          tbl = tbl.sort_values(columns_to_sort)
          tbl = pa.Table.from_pandas(tbl, schema)
      return tbl
  
imports: import copy
  import random
  import string
  import uuid
  from multiprocessing import Pool
  import numpy
  import pandas
  import pyarrow
  from pyarrow import parquet
  import cudf
  from cudf.utils.dtypes import np_to_pa_dtype
label_desc: Use image node_id 4 for calling a global function with example usage: get_dataframe(parameters, use_threads) and returns: tbl