rasa/core/policies/embedding_policy.py
def _create_all_Y_d(self, dialogue_len: int) -> np.ndarray:
        """Stack encoded_all_intents on top of each other

        to create candidates for training examples and
        to calculate training accuracy.
        """

        return np.stack([self.encoded_all_actions] * dialogue_len)
Stack encoded_all_intents on top of each other

        to create candidates for training examples and
        to calculate training accuracy.
        
Stack encoded_all_intents on top of each other
to create candidates for training examples and
to calculate training accuracy.
Stack encoded_all_intents on top of each other to create candidates for training examples and to calculate training accuracy .
def _create_all_Y_d(self, dialogue_len: int) -> np.ndarray:
        return np.stack([self.encoded_all_actions] * dialogue_len)
def _create_all_Y_d ( self , dialogue_len : int ) -> np . ndarray : return np . stack ( [ self . encoded_all_actions ] * dialogue_len )
Stack encoded_all_intents on top of each other
to create candidates for training examples and
to calculate training accuracy.
Stack encoded_all_intents on top of each other to create candidates for training examples and to calculate training accuracy .
"""Stack encoded_all_intents on top of each other

        to create candidates for training examples and
        to calculate training accuracy.
        """
self
dialogue_len
int