parameters: (a, perm=None, name="transpose")
docstring: Transposes `a`. Permutes the dimensions according to `perm`.
  
    The returned tensor's dimension i will correspond to the input dimension
    `perm[i]`. If `perm` is not given, it is set to (n-1...0), where n is
    the rank of the input tensor. Hence by default, this operation performs a
    regular matrix transpose on 2-D input Tensors.
  
    For example:
  
    ```python
    # 'x' is [[1 2 3]
    #         [4 5 6]]
    tf.transpose(x) ==> [[1 4]
                         [2 5]
                         [3 6]]
  
    # Equivalently
    tf.transpose(x, perm=[1, 0]) ==> [[1 4]
                                      [2 5]
                                      [3 6]]
  
    # 'perm' is more useful for n-dimensional tensors, for n > 2
    # 'x' is   [[[1  2  3]
    #            [4  5  6]]
    #           [[7  8  9]
    #            [10 11 12]]]
    # Take the transpose of the matrices in dimension-0
    tf.transpose(x, perm=[0, 2, 1]) ==> [[[1  4]
                                          [2  5]
                                          [3  6]]
  
                                         [[7 10]
                                          [8 11]
                                          [9 12]]]
    ```
  
    Args:
      a: A `Tensor`.
      perm: A permutation of the dimensions of `a`.
      name: A name for the operation (optional).
  
    Returns:
      A transposed `Tensor`.
docstring_summary: Transposes `a`. Permutes the dimensions according to `perm`.
docstring_tokens: Transposes a . Permutes the dimensions according to perm .
function: def transpose(a, perm=None, name="transpose"):
    """Transposes `a`. Permutes the dimensions according to `perm`.
  
    The returned tensor's dimension i will correspond to the input dimension
    `perm[i]`. If `perm` is not given, it is set to (n-1...0), where n is
    the rank of the input tensor. Hence by default, this operation performs a
    regular matrix transpose on 2-D input Tensors.
  
    For example:
  
    ```python
    # 'x' is [[1 2 3]
    #         [4 5 6]]
    tf.transpose(x) ==> [[1 4]
                         [2 5]
                         [3 6]]
  
    # Equivalently
    tf.transpose(x, perm=[1, 0]) ==> [[1 4]
                                      [2 5]
                                      [3 6]]
  
    # 'perm' is more useful for n-dimensional tensors, for n > 2
    # 'x' is   [[[1  2  3]
    #            [4  5  6]]
    #           [[7  8  9]
    #            [10 11 12]]]
    # Take the transpose of the matrices in dimension-0
    tf.transpose(x, perm=[0, 2, 1]) ==> [[[1  4]
                                          [2  5]
                                          [3  6]]
  
                                         [[7 10]
                                          [8 11]
                                          [9 12]]]
    ```
  
    Args:
      a: A `Tensor`.
      perm: A permutation of the dimensions of `a`.
      name: A name for the operation (optional).
  
    Returns:
      A transposed `Tensor`.
    """
    with ops.name_scope(name, "transpose", [a]) as name:
      if perm is None:
        rank = gen_array_ops.rank(a)
        perm = (rank - 1) - gen_math_ops._range(0, rank, 1)
        ret = gen_array_ops.transpose(a, perm, name=name)
        # NOTE(mrry): Setting the shape explicitly because
        #   reverse is not handled by the shape function.
        input_shape = ret.op.inputs[0].get_shape().dims
        if input_shape is not None:
          ret.set_shape(input_shape[::-1])
      else:
        ret = gen_array_ops.transpose(a, perm, name=name)
      return ret
function_tokens: def transpose ( a , perm = None , name = "transpose" ) : with ops . name_scope ( name , "transpose" , [ a ] ) as name : if perm is None : rank = gen_array_ops . rank ( a ) perm = ( rank - 1 ) - gen_math_ops . _range ( 0 , rank , 1 ) ret = gen_array_ops . transpose ( a , perm , name = name ) # NOTE(mrry): Setting the shape explicitly because #   reverse is not handled by the shape function. input_shape = ret . op . inputs [ 0 ] . get_shape ( ) . dims if input_shape is not None : ret . set_shape ( input_shape [ : : - 1 ] ) else : ret = gen_array_ops . transpose ( a , perm , name = name ) return ret