class_docstr: matrix(data, dtype=None, copy=True)
  
  Returns a matrix from an array-like object, or from a string of data.
  
  A matrix is a specialized 2-D array that retains its 2-D nature
  through operations.  It has certain special operators, such as ``*``
  (matrix multiplication) and ``**`` (matrix power).
  
  .. note:: It is no longer recommended to use this class, even for linear
            algebra. Instead use regular arrays. The class may be removed
            in the future.
  
  Parameters
  ----------
  data : array_like or string
     If `data` is a string, it is interpreted as a matrix with commas
     or spaces separating columns, and semicolons separating rows.
  dtype : data-type
     Data-type of the output matrix.
  copy : bool
     If `data` is already an `ndarray`, then this flag determines
     whether the data is copied (the default), or whether a view is
     constructed.
  
  See Also
  --------
  array
  
  Examples
  --------
  >>> a = np.matrix('1 2; 3 4')
  >>> a
  matrix([[1, 2],
          [3, 4]])
  
  >>> np.matrix([[1, 2], [3, 4]])
  matrix([[1, 2],
          [3, 4]])
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docstr: Returns the (complex) conjugate transpose of `self`.
  
  Equivalent to ``np.transpose(self)`` if `self` is real-valued.
  
  Parameters
  ----------
  None
  
  Returns
  -------
  ret : matrix object
      complex conjugate transpose of `self`
  
  Examples
  --------
  >>> x = np.matrix(np.arange(12).reshape((3,4)))
  >>> z = x - 1j*x; z
  matrix([[  0. +0.j,   1. -1.j,   2. -2.j,   3. -3.j],
          [  4. -4.j,   5. -5.j,   6. -6.j,   7. -7.j],
          [  8. -8.j,   9. -9.j,  10.-10.j,  11.-11.j]])
  >>> z.getH()
  matrix([[ 0. -0.j,  4. +4.j,  8. +8.j],
          [ 1. +1.j,  5. +5.j,  9. +9.j],
          [ 2. +2.j,  6. +6.j, 10.+10.j],
          [ 3. +3.j,  7. +7.j, 11.+11.j]])
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code: def H(self):
      """
      Returns the (complex) conjugate transpose of `self`.
  
      Equivalent to ``np.transpose(self)`` if `self` is real-valued.
  
      Parameters
      ----------
      None
  
      Returns
      -------
      ret : matrix object
          complex conjugate transpose of `self`
  
      Examples
      --------
      >>> x = np.matrix(np.arange(12).reshape((3,4)))
      >>> z = x - 1j*x; z
      matrix([[  0. +0.j,   1. -1.j,   2. -2.j,   3. -3.j],
              [  4. -4.j,   5. -5.j,   6. -6.j,   7. -7.j],
              [  8. -8.j,   9. -9.j,  10.-10.j,  11.-11.j]])
      >>> z.getH()
      matrix([[ 0. -0.j,  4. +4.j,  8. +8.j],
              [ 1. +1.j,  5. +5.j,  9. +9.j],
              [ 2. +2.j,  6. +6.j, 10.+10.j],
              [ 3. +3.j,  7. +7.j, 11.+11.j]])
  
      """
      if issubclass(self.dtype.type, N.complexfloating):
          return self.transpose().conjugate()
      else:
          return self.transpose()
  
imports: import sys
  import warnings
  import ast
  from .._utils import set_module
  import numpy._core.numeric
  from numpy._core.numeric import concatenate, isscalar
  from numpy.linalg import matrix_power
label_desc: Use image node_id 32 for calling the matrix obj's underlying member method code with example usage: obj.H() and returns: self, self