code: def transpose(a, axes=None):
      
      if isinstance(a, np.ndarray):
          return np.transpose(a, axes)
      elif isinstance(a, RemoteArray):
          return a.transpose(*axes)
      elif isinstance(a, Remote):
          return _remote_to_array(a).transpose(*axes)
      elif isinstance(a, DistArray):
          if axes is None:
              axes = range(a.ndim - 1, -1, -1)
          axes = list(axes)
          if len(set(axes)) < len(axes):
              raise ValueError("repeated axis in transpose")
          if sorted(axes) != list(range(a.ndim)):
              raise ValueError("axes don't match array")
          distaxis = a._distaxis
          new_distaxis = axes.index(distaxis)
          new_subarrays = [ra.transpose(*axes) for ra in a._subarrays]
          return DistArray(new_subarrays, new_distaxis)
      else:
          return np.transpose(a, axes)
docs: Returns a view of the array with axes transposed.
  
      For a 1-D array, this has no effect.
      For a 2-D array, this is the usual matrix transpose.
      For an n-D array, if axes are given, their order indicates how the
      axes are permuted
  
      Args:
        a (array_like): Input array.
        axes (list of int, optional): By default, reverse the dimensions,
          otherwise permute the axes according to the values given.