(self, tensor, layer_parallelism)
return None
For backends which support distributed training, this will distribute
        or gather the error or activation tensor depending on the type of
        parallelism used to distribute the layer computation. Currently
        this is only supported by multi-GPU in Nervana cloud.

        Arguments:
            tensor: Tensor containing either activations or errors
            layer_parallelism: Type of parallelism expected by the layer

        Returns:
            Tensor which has been altered by this call or None
For backends which support distributed training, this will distribute
        or gather the error or activation tensor depending on the type of
        parallelism used to distribute the layer computation. Currently
        this is only supported by multi-GPU in Nervana cloud.
For backends which support distributed training this will distribute or gather the error or activation tensor depending on the type of parallelism used to distribute the layer computation . Currently this is only supported by multi - GPU in Nervana cloud .
def distribute_data(self, tensor, layer_parallelism):
        """
        For backends which support distributed training, this will distribute
        or gather the error or activation tensor depending on the type of
        parallelism used to distribute the layer computation. Currently
        this is only supported by multi-GPU in Nervana cloud.

        Arguments:
            tensor: Tensor containing either activations or errors
            layer_parallelism: Type of parallelism expected by the layer

        Returns:
            Tensor which has been altered by this call or None
        """
        return None
def distribute_data ( self , tensor , layer_parallelism ) : return None