(self, inputs, inference=False)
return self.outputs
fprop for deconv is equivalent to bprop for conv.
        bprop_conv takes in error and deltas as "E" and "grad_I"
        for deconv, bprop_conv will take in input as "E" and output as "grad_I"

        Arguments:
            inference (bool): is inference only
            inputs (Tensor): input data

        Returns:
            Tensor: output data
fprop for deconv is equivalent to bprop for conv.
        bprop_conv takes in error and deltas as "E" and "grad_I"
        for deconv, bprop_conv will take in input as "E" and output as "grad_I"
fprop for deconv is equivalent to bprop for conv . bprop_conv takes in error and deltas as E and grad_I for deconv bprop_conv will take in input as E and output as grad_I
def fprop(self, inputs, inference=False):
        """
        fprop for deconv is equivalent to bprop for conv.
        bprop_conv takes in error and deltas as "E" and "grad_I"
        for deconv, bprop_conv will take in input as "E" and output as "grad_I"

        Arguments:
            inference (bool): is inference only
            inputs (Tensor): input data

        Returns:
            Tensor: output data
        """
        self.inputs = inputs
        self.be.bprop_conv(layer=self.nglayer, F=self.W, E=inputs, grad_I=self.outputs,
                           bsum=self.batch_sum)
        return self.outputs
def fprop ( self , inputs , inference = False ) : self . inputs = inputs self . be . bprop_conv ( layer = self . nglayer , F = self . W , E = inputs , grad_I = self . outputs , bsum = self . batch_sum ) return self . outputs