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: Indexes of the minimum values along an axis.
  
  Return the indexes of the first occurrences of the minimum values
  along the specified axis.  If axis is None, the index is for the
  flattened matrix.
  
  Parameters
  ----------
  See `numpy.argmin` for complete descriptions.
  
  See Also
  --------
  numpy.argmin
  
  Notes
  -----
  This is the same as `ndarray.argmin`, but returns a `matrix` object
  where `ndarray.argmin` 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.argmin()
  11
  >>> x.argmin(0)
  matrix([[2, 2, 2, 2]])
  >>> x.argmin(1)
  matrix([[3],
          [3],
          [3]])
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code: def argmin(self, axis=None, out=None):
      """
      Indexes of the minimum values along an axis.
  
      Return the indexes of the first occurrences of the minimum values
      along the specified axis.  If axis is None, the index is for the
      flattened matrix.
  
      Parameters
      ----------
      See `numpy.argmin` for complete descriptions.
  
      See Also
      --------
      numpy.argmin
  
      Notes
      -----
      This is the same as `ndarray.argmin`, but returns a `matrix` object
      where `ndarray.argmin` 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.argmin()
      11
      >>> x.argmin(0)
      matrix([[2, 2, 2, 2]])
      >>> x.argmin(1)
      matrix([[3],
              [3],
              [3]])
  
      """
      return N.ndarray.argmin(self, axis, out)._align(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 25 for calling the matrix obj's underlying member method code with example usage: obj.argmin(axis, out) and returns: N