parameters: (self,
                            features,
                            mode,
                            labels=None,
                            train_op_fn=None,
                            logits=None,
                            logits_input=None,
                            scope=None)
docstring: Returns `ModelFnOps` that a model_fn can return.
  
      Please note that,
      + Exactly one of `logits` and `logits_input` must be provided.
      + All args must be passed via name.
  
      Args:
        features: Input `dict` of `Tensor` objects.
        mode: Estimator's `ModeKeys`.
        labels: Labels `Tensor`, or `dict` of same.
        train_op_fn: Function that takes a scalar loss `Tensor` and returns an op
            to optimize the model with the loss. This is used in TRAIN mode and
            must not be None. None is allowed in other modes. If you want to
            optimize loss yourself you can pass `no_op_train_fn` and then use
            ModeFnOps.loss to compute and apply gradients.
        logits: logits `Tensor` to be used by the head.
        logits_input: `Tensor` from which to build logits, often needed when you
          don't want to compute the logits. Typically this is the activation of
          the last hidden layer in a DNN. Some heads (like the ones responsible
          for candidate sampling) intrinsically avoid computing full logits and
          only accepts logits_input.
        scope: Optional scope for `variable_scope`.
  
      Returns:
        An instance of `ModelFnOps`.
  
      Raises:
        ValueError: If `mode` is not recognized.
        ValueError: If neither or both of `logits` and `logits_input` is provided.
docstring_summary: Returns `ModelFnOps` that a model_fn can return.
docstring_tokens: Returns ModelFnOps that a model_fn can return .
function: def create_model_fn_ops(self,
                            features,
                            mode,
                            labels=None,
                            train_op_fn=None,
                            logits=None,
                            logits_input=None,
                            scope=None):
      """Returns `ModelFnOps` that a model_fn can return.
  
      Please note that,
      + Exactly one of `logits` and `logits_input` must be provided.
      + All args must be passed via name.
  
      Args:
        features: Input `dict` of `Tensor` objects.
        mode: Estimator's `ModeKeys`.
        labels: Labels `Tensor`, or `dict` of same.
        train_op_fn: Function that takes a scalar loss `Tensor` and returns an op
            to optimize the model with the loss. This is used in TRAIN mode and
            must not be None. None is allowed in other modes. If you want to
            optimize loss yourself you can pass `no_op_train_fn` and then use
            ModeFnOps.loss to compute and apply gradients.
        logits: logits `Tensor` to be used by the head.
        logits_input: `Tensor` from which to build logits, often needed when you
          don't want to compute the logits. Typically this is the activation of
          the last hidden layer in a DNN. Some heads (like the ones responsible
          for candidate sampling) intrinsically avoid computing full logits and
          only accepts logits_input.
        scope: Optional scope for `variable_scope`.
  
      Returns:
        An instance of `ModelFnOps`.
  
      Raises:
        ValueError: If `mode` is not recognized.
        ValueError: If neither or both of `logits` and `logits_input` is provided.
      """
      raise NotImplementedError("Calling an abstract method.")
function_tokens: def create_model_fn_ops ( self , features , mode , labels = None , train_op_fn = None , logits = None , logits_input = None , scope = None ) : raise NotImplementedError ( "Calling an abstract method." )