parameters: (matrix, name=None)
docstring: Transposes the last two dimensions of and conjugates tensor `matrix`.
  
    For example:
  
    ```python
    x = tf.constant([[1 + 1j, 2 + 2j, 3 + 3j],
                     [4 + 4j, 5 + 5j, 6 + 6j]])
    tf.linalg.adjoint(x)  # [[1 - 1j, 4 - 4j],
                          #  [2 - 2j, 5 - 5j],
                          #  [3 - 3j, 6 - 6j]]
    ```
  
    Args:
      matrix:  A `Tensor`. Must be `float16`, `float32`, `float64`, `complex64`,
        or `complex128` with shape `[..., M, M]`.
      name:  A name to give this `Op` (optional).
  
    Returns:
      The adjoint (a.k.a. Hermitian transpose a.k.a. conjugate transpose) of
      matrix.
docstring_summary: Transposes the last two dimensions of and conjugates tensor `matrix`.
docstring_tokens: Transposes the last two dimensions of and conjugates tensor matrix .
function: def adjoint(matrix, name=None):
    """Transposes the last two dimensions of and conjugates tensor `matrix`.
  
    For example:
  
    ```python
    x = tf.constant([[1 + 1j, 2 + 2j, 3 + 3j],
                     [4 + 4j, 5 + 5j, 6 + 6j]])
    tf.linalg.adjoint(x)  # [[1 - 1j, 4 - 4j],
                          #  [2 - 2j, 5 - 5j],
                          #  [3 - 3j, 6 - 6j]]
    ```
  
    Args:
      matrix:  A `Tensor`. Must be `float16`, `float32`, `float64`, `complex64`,
        or `complex128` with shape `[..., M, M]`.
      name:  A name to give this `Op` (optional).
  
    Returns:
      The adjoint (a.k.a. Hermitian transpose a.k.a. conjugate transpose) of
      matrix.
    """
    with ops.name_scope(name, 'adjoint', [matrix]):
      matrix = ops.convert_to_tensor(matrix, name='matrix')
      return array_ops.matrix_transpose(matrix, conjugate=True)
function_tokens: def adjoint ( matrix , name = None ) : with ops . name_scope ( name , 'adjoint' , [ matrix ] ) : matrix = ops . convert_to_tensor ( matrix , name = 'matrix' ) return array_ops . matrix_transpose ( matrix , conjugate = True )