class_docstr: matrix(data, dtype=None, copy=True)
  
  .. 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.
  
  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).
  
  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')
  >>> print(a)
  [[1 2]
   [3 4]]
  
  >>> np.matrix([[1, 2], [3, 4]])
  matrix([[1, 2],
          [3, 4]])
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docstr: Return a possibly reshaped matrix.
  
  Refer to `numpy.squeeze` for more documentation.
  
  Parameters
  ----------
  axis : None or int or tuple of ints, optional
      Selects a subset of the single-dimensional entries in the shape.
      If an axis is selected with shape entry greater than one,
      an error is raised.
  
  Returns
  -------
  squeezed : matrix
      The matrix, but as a (1, N) matrix if it had shape (N, 1).
  
  See Also
  --------
  numpy.squeeze : related function
  
  Notes
  -----
  If `m` has a single column then that column is returned
  as the single row of a matrix.  Otherwise `m` is returned.
  The returned matrix is always either `m` itself or a view into `m`.
  Supplying an axis keyword argument will not affect the returned matrix
  but it may cause an error to be raised.
  
  Examples
  --------
  >>> c = np.matrix([[1], [2]])
  >>> c
  matrix([[1],
          [2]])
  >>> c.squeeze()
  matrix([[1, 2]])
  >>> r = c.T
  >>> r
  matrix([[1, 2]])
  >>> r.squeeze()
  matrix([[1, 2]])
  >>> m = np.matrix([[1, 2], [3, 4]])
  >>> m.squeeze()
  matrix([[1, 2],
          [3, 4]])
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code: def squeeze(self, axis=None):
      """
      Return a possibly reshaped matrix.
  
      Refer to `numpy.squeeze` for more documentation.
  
      Parameters
      ----------
      axis : None or int or tuple of ints, optional
          Selects a subset of the single-dimensional entries in the shape.
          If an axis is selected with shape entry greater than one,
          an error is raised.
  
      Returns
      -------
      squeezed : matrix
          The matrix, but as a (1, N) matrix if it had shape (N, 1).
  
      See Also
      --------
      numpy.squeeze : related function
  
      Notes
      -----
      If `m` has a single column then that column is returned
      as the single row of a matrix.  Otherwise `m` is returned.
      The returned matrix is always either `m` itself or a view into `m`.
      Supplying an axis keyword argument will not affect the returned matrix
      but it may cause an error to be raised.
  
      Examples
      --------
      >>> c = np.matrix([[1], [2]])
      >>> c
      matrix([[1],
              [2]])
      >>> c.squeeze()
      matrix([[1, 2]])
      >>> r = c.T
      >>> r
      matrix([[1, 2]])
      >>> r.squeeze()
      matrix([[1, 2]])
      >>> m = np.matrix([[1, 2], [3, 4]])
      >>> m.squeeze()
      matrix([[1, 2],
              [3, 4]])
  
      """
      return N.ndarray.squeeze(self, axis=axis)
  
imports: from __future__ import division, absolute_import, print_function
  import sys
  import warnings
  import ast
  import numpy.core.numeric
  from numpy.core.numeric import concatenate, isscalar
  from numpy.core.overrides import set_module
  from numpy.linalg import matrix_power
label_desc: Use image node_id 14 for calling the matrix obj's underlying member method code with example usage: obj.squeeze(axis) and returns: N