(self, A, B, C, alpha=1.0, beta=0.0, relu=False)
Perform one of the following operations:
        1 For fprop: A(K, C), B(X,C,N), C(X,K,N) --> call batched_dot(A, B, C)
        2 For bprop: A(K, C), B(X,K,N), C(X,C,N) --> call batched_dot(A.T, B, C)
        3 For update: A(X,K,N), B(X,C,N), C(K,C) --> call batched_dot(A, B.T, C)

        Arguments:
            A (Tensor): left-hand input operand
            B (Tensor): right-hand input operand
            C (Tensor): output operand
            alpha (float. optional): scale A*B term
            beta (float, optional): scale C term before sum
            relu (bool, optional): If True apply ReLu non-linearity before
                                   output.  Defaults to False.
Perform one of the following operations:
        1 For fprop: A(K, C), B(X,C,N), C(X,K,N) --> call batched_dot(A, B, C)
        2 For bprop: A(K, C), B(X,K,N), C(X,C,N) --> call batched_dot(A.T, B, C)
        3 For update: A(X,K,N), B(X,C,N), C(K,C) --> call batched_dot(A, B.T, C)
Perform one of the following operations : 1 For fprop : A ( K C ) B ( X C N ) C ( X K N ) -- > call batched_dot ( A B C ) 2 For bprop : A ( K C ) B ( X K N ) C ( X C N ) -- > call batched_dot ( A . T B C ) 3 For update : A ( X K N ) B ( X C N ) C ( K C ) -- > call batched_dot ( A B . T C )
def batched_dot(self, A, B, C, alpha=1.0, beta=0.0, relu=False):
        """
        Perform one of the following operations:
        1 For fprop: A(K, C), B(X,C,N), C(X,K,N) --> call batched_dot(A, B, C)
        2 For bprop: A(K, C), B(X,K,N), C(X,C,N) --> call batched_dot(A.T, B, C)
        3 For update: A(X,K,N), B(X,C,N), C(K,C) --> call batched_dot(A, B.T, C)

        Arguments:
            A (Tensor): left-hand input operand
            B (Tensor): right-hand input operand
            C (Tensor): output operand
            alpha (float. optional): scale A*B term
            beta (float, optional): scale C term before sum
            relu (bool, optional): If True apply ReLu non-linearity before
                                   output.  Defaults to False.
        """
        raise NotImplementedError()
def batched_dot ( self , A , B , C , alpha = 1.0 , beta = 0.0 , relu = False ) : raise NotImplementedError ( )