parameters: (x, *axis)
return_statement: return F.transpose(x, perm)
docstring: r"""
      Return a view of the tensor with axes transposed.
  
      For a 1-D tensor this has no effect, as a transposed vector is simply the
      same vector. For a 2-D tensor, this is a standard matrix transpose. For a
      n-D tensor, if axes are given, their order indicates how the axes are permuted.
      If axes are not provided and tensor.shape = (i[0], i[1],...i[n-2], i[n-1]),
      then tensor.transpose().shape = (i[n-1], i[n-2], ... i[1], i[0]).
  
      Args:
          axes(Union[None, tuple(int), list(int), int], optional): If axes is None or
              blank, tensor.transpose() will reverse the order of the axes. If axes is tuple(int)
              or list(int), tensor.transpose() will transpose the tensor to the new axes order.
              If axes is int, this form is simply intended as a convenience alternative to the
              tuple/list form.
  
      Returns:
          Tensor, has the same dimension as input tensor, with axes suitably permuted.
  
      Raises:
          TypeError: If input arguments have types not specified above.
          ValueError: If the number of `axes` is not euqal to a.ndim.
  
      Supported Platforms:
          ``Ascend`` ``GPU`` ``CPU``
  
      Examples:
          >>> import numpy as np
          >>> from mindspore import Tensor
          >>> x = Tensor(np.ones((1,2,3), dtype=np.float32))
          >>> x = x.transpose()
          >>> print(x.shape)
          (3, 2, 1)
docstring_summary: r"""
      Return a view of the tensor with axes transposed.
docstring_tokens: r Return a view of the tensor with axes transposed .
function: def transpose(x, *axis):
      r"""
      Return a view of the tensor with axes transposed.
  
      For a 1-D tensor this has no effect, as a transposed vector is simply the
      same vector. For a 2-D tensor, this is a standard matrix transpose. For a
      n-D tensor, if axes are given, their order indicates how the axes are permuted.
      If axes are not provided and tensor.shape = (i[0], i[1],...i[n-2], i[n-1]),
      then tensor.transpose().shape = (i[n-1], i[n-2], ... i[1], i[0]).
  
      Args:
          axes(Union[None, tuple(int), list(int), int], optional): If axes is None or
              blank, tensor.transpose() will reverse the order of the axes. If axes is tuple(int)
              or list(int), tensor.transpose() will transpose the tensor to the new axes order.
              If axes is int, this form is simply intended as a convenience alternative to the
              tuple/list form.
  
      Returns:
          Tensor, has the same dimension as input tensor, with axes suitably permuted.
  
      Raises:
          TypeError: If input arguments have types not specified above.
          ValueError: If the number of `axes` is not euqal to a.ndim.
  
      Supported Platforms:
          ``Ascend`` ``GPU`` ``CPU``
  
      Examples:
          >>> import numpy as np
          >>> from mindspore import Tensor
          >>> x = Tensor(np.ones((1,2,3), dtype=np.float32))
          >>> x = x.transpose()
          >>> print(x.shape)
          (3, 2, 1)
      """
      ndim = F.rank(x)
      perm = check_transpose_axis_const(axis, ndim)
      return F.transpose(x, perm)
function_tokens: def transpose ( x , * axis ) : ndim = F . rank ( x ) perm = check_transpose_axis_const ( axis , ndim ) return F . transpose ( x , perm )