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: Return the maximum of the array/matrix or maximum along an axis, ignoring any
  NaNs. This takes all elements into account, not just the non-zero
  ones.
  
  .. versionadded:: 1.11.0
  
  Parameters
  ----------
  axis : {-2, -1, 0, 1, None} optional
      Axis along which the maximum is computed. The default is to
      compute the maximum over all 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.
  
  Returns
  -------
  amax : coo_matrix or scalar
      Maximum of `a`. If `axis` is None, the result is a scalar value.
      If `axis` is given, the result is a sparse.coo_matrix of dimension
      ``a.ndim - 1``.
  
  See Also
  --------
  nanmin : The minimum value of a sparse array/matrix along a given axis,
           ignoring NaNs.
  max : The maximum value of a sparse array/matrix along a given axis,
        propagating NaNs.
  numpy.nanmax : NumPy's implementation of 'nanmax'.
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code: def nanmax(self, axis=None, out=None):
      """
      Return the maximum of the array/matrix or maximum along an axis, ignoring any
      NaNs. This takes all elements into account, not just the non-zero
      ones.
  
      .. versionadded:: 1.11.0
  
      Parameters
      ----------
      axis : {-2, -1, 0, 1, None} optional
          Axis along which the maximum is computed. The default is to
          compute the maximum over all 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.
  
      Returns
      -------
      amax : coo_matrix or scalar
          Maximum of `a`. If `axis` is None, the result is a scalar value.
          If `axis` is given, the result is a sparse.coo_matrix of dimension
          ``a.ndim - 1``.
  
      See Also
      --------
      nanmin : The minimum value of a sparse array/matrix along a given axis,
               ignoring NaNs.
      max : The maximum value of a sparse array/matrix along a given axis,
            propagating NaNs.
      numpy.nanmax : NumPy's implementation of 'nanmax'.
  
      """
      return self._min_or_max(axis, out, np.fmax)
  
imports: import numpy
  from ._base import _spbase, _ufuncs_with_fixed_point_at_zero
  from ._sputils import isscalarlike, validateaxis
label_desc: Use image node_id 7 for calling the _minmax_mixin obj's underlying member method code with example usage: obj.nanmax(axis, out) and returns: self