class_docstr: The interface of a mixin class.
docstr: Perform one training step over data.
  
  Returns
  -------
  (LP, LA) : tuple of (float, float)
      predictor loss and adversary loss.
docstr_tok: ["Perform","one","training","step","over","data",".","Returns","--","--","--","-","(","LP",",","LA",")",":","tuple","of","(","float",",","float",")","predictor","loss","and","adversary","loss","."]
code: def train_step(self, X: ndarray, Y: ndarray, A: ndarray):
      """
      Perform one training step over data.
  
      Returns
      -------
      (LP, LA) : tuple of (float, float)
          predictor loss and adversary loss.
      """
      raise NotImplementedError(_NOT_IMPLEMENTED)
  
file_path: fairlearn/fairlearn/adversarial/_backend_engine.py
imports: from ._constants import _KWARG_ERROR_MESSAGE, _NOT_IMPLEMENTED, _DIST_TYPE_NOT_IMPLEMENTED, _NO_LOSS, _LIST_MODEL_UNSUPPORTED
  from sklearn.utils import shuffle
  from numpy import ndarray
label_desc: Use image node_id 7 for calling the BackendEngine obj's underlying member method code with example usage: obj.train_step(X, Y, A) without return types