code: def transpose(*args):
      """
      Transpose index and columns.
  
      Reflect the DataFrame over its main diagonal by writing rows as columns
      and vice-versa. The property :attr:`.T` is an accessor to the method
      :meth:`transpose`.
  
      Parameters
      ----------
      *args : tuple, optional
              Accepted for compatibility with NumPy.
  
      Returns
      -------
      DataFrame
          The transposed DataFrame.
  
      See Also
      --------
      numpy.transpose : Permute the dimensions of a given array.
  
      Notes
      -----
      Transposing a DataFrame with mixed dtypes will result in a homogeneous
      DataFrame with the `object` dtype.
  
      Examples
      --------
      **Square DataFrame with homogeneous dtype**
  
      >>> import mars.dataframe as md
      >>> d1 = {'col1': [1, 2], 'col2': [3, 4]}
      >>> df1 = md.DataFrame(data=d1).execute()
      >>> df1
          col1  col2
      0     1     3
      1     2     4
  
      >>> df1_transposed = df1.T.execute() # or df1.transpose().execute()
      >>> df1_transposed
            0  1
      col1  1  2
      col2  3  4
  
      When the dtype is homogeneous in the original DataFrame, we get a
      transposed DataFrame with the same dtype:
  
      >>> df1.dtypes
      col1    int64
      col2    int64
      dtype: object
  
      >>> df1_transposed.dtypes
      0    int64
      1    int64
      dtype: object
  
      **Non-square DataFrame with mixed dtypes**
  
      >>> d2 = {'name': ['Alice', 'Bob'],
      ...       'score': [9.5, 8],
      ...       'employed': [False, True],
      ...       'kids': [0, 0]}
      >>> df2 = md.DataFrame(data=d2).execute()
      >>> df2
          name  score  employed  kids
      0  Alice    9.5     False     0
      1    Bob    8.0      True     0
  
      >>> df2_transposed = df2.T.execute() # or df2.transpose().execute()
      >>> df2_transposed
                    0     1
      name      Alice   Bob
      score       9.5   8.0
      employed  False  True
      kids          0     0
  
      When the DataFrame has mixed dtypes, we get a transposed DataFrame with
      the `object` dtype:
  
      >>> df2.dtypes
      name         object
      score       float64
      employed       bool
      kids          int64
      dtype: object
  
      >>> df2_transposed.dtypes
      0    object
      1    object
      dtype: object
      """
      op = DataFrameTranspose()
      return op(args)
  
file_path: mars/mars/dataframe/base/transpose.py
imports: import numpy
  from ...None import opcodes
  from ...core import OutputType
  from ..operands import DataFrameOperand, DataFrameOperandMixin
  from ..utils import parse_index
label_desc: Use image node_id 1 for calling a global function with example usage: transpose() and returns: op