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 the minimum value along an axis.
  
  Parameters
  ----------
  See `amin` for complete descriptions.
  
  See Also
  --------
  amin, ndarray.min
  
  Notes
  -----
  This is the same as `ndarray.min`, but returns a `matrix` object
  where `ndarray.min` would return an ndarray.
  
  Examples
  --------
  >>> x = -np.matrix(np.arange(12).reshape((3,4))); x
  matrix([[  0,  -1,  -2,  -3],
          [ -4,  -5,  -6,  -7],
          [ -8,  -9, -10, -11]])
  >>> x.min()
  -11
  >>> x.min(0)
  matrix([[ -8,  -9, -10, -11]])
  >>> x.min(1)
  matrix([[ -3],
          [ -7],
          [-11]])
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code: def min(self, axis=None, out=None):
      """
      Return the minimum value along an axis.
  
      Parameters
      ----------
      See `amin` for complete descriptions.
  
      See Also
      --------
      amin, ndarray.min
  
      Notes
      -----
      This is the same as `ndarray.min`, but returns a `matrix` object
      where `ndarray.min` would return an ndarray.
  
      Examples
      --------
      >>> x = -np.matrix(np.arange(12).reshape((3,4))); x
      matrix([[  0,  -1,  -2,  -3],
              [ -4,  -5,  -6,  -7],
              [ -8,  -9, -10, -11]])
      >>> x.min()
      -11
      >>> x.min(0)
      matrix([[ -8,  -9, -10, -11]])
      >>> x.min(1)
      matrix([[ -3],
              [ -7],
              [-11]])
  
      """
      return N.ndarray.min(self, axis, out, keepdims=True)._collapse(
          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 24 for calling the matrix obj's underlying member method code with example usage: obj.min(axis, out) and returns: N