class_docstr: Mixin for min and max methods.
  These are not implemented for dia_matrix, hence the separate class.
class_docstr_tok: ["Mixin","for","min","and","max","methods",".","These","are","not","implemented","for","dia_matrix",",","hence","the","separate","class","."]
docstr: Returns the maximum of the matrix or maximum along an axis.
  
  Args:
      axis (int): {-2, -1, 0, 1, ``None``} (optional)
          Axis along which the sum is computed. The default is to
          compute the maximum over all the matrix elements, returning
          a scalar (i.e. ``axis`` = ``None``).
      out (None): (optional)
          This argument is in the signature *solely* for NumPy
          compatibility reasons. Do not pass in anything except
          for the default value, as this argument is not used.
      explicit (bool): Return the maximum value explicitly specified and
          ignore all implicit zero entries. If the dimension has no
          explicit values, a zero is then returned to indicate that it is
          the only implicit value. This parameter is experimental and may
          change in the future.
  
  Returns:
      (cupy.ndarray or float): Maximum of ``a``. If ``axis`` is
      ``None``, the result is a scalar value. If ``axis`` is given,
      the result is an array of dimension ``a.ndim - 1``. This
      differs from numpy for computational efficiency.
  
  .. seealso:: min : The minimum value of a sparse matrix along a given
    axis.
  .. seealso:: numpy.matrix.max : NumPy's implementation of ``max`` for
    matrices
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code: def max(self, axis=None, out=None, *, explicit=False):
      """Returns the maximum of the matrix or maximum along an axis.
  
      Args:
          axis (int): {-2, -1, 0, 1, ``None``} (optional)
              Axis along which the sum is computed. The default is to
              compute the maximum over all the matrix elements, returning
              a scalar (i.e. ``axis`` = ``None``).
          out (None): (optional)
              This argument is in the signature *solely* for NumPy
              compatibility reasons. Do not pass in anything except
              for the default value, as this argument is not used.
          explicit (bool): Return the maximum value explicitly specified and
              ignore all implicit zero entries. If the dimension has no
              explicit values, a zero is then returned to indicate that it is
              the only implicit value. This parameter is experimental and may
              change in the future.
  
      Returns:
          (cupy.ndarray or float): Maximum of ``a``. If ``axis`` is
          ``None``, the result is a scalar value. If ``axis`` is given,
          the result is an array of dimension ``a.ndim - 1``. This
          differs from numpy for computational efficiency.
  
      .. seealso:: min : The minimum value of a sparse matrix along a given
        axis.
      .. seealso:: numpy.matrix.max : NumPy's implementation of ``max`` for
        matrices
  
      """
      if explicit:
          api_name = "explicit of cupyx.scipy.sparse.{}.max".format(
              self.__class__.__name__
          )
          _util.experimental(api_name)
      return self._min_or_max(axis, out, cupy.max, explicit)
  
imports: import cupy
  import numpy
  from cupy._core import internal
  from cupy import _util
  from cupyx.scipy.sparse import _base
  from cupyx.scipy.sparse import _coo
  from cupyx.scipy.sparse import _sputils
label_desc: Use image node_id 5 for calling the _minmax_mixin obj's underlying member method code with example usage: obj.max(axis, out) and returns: self