def METHOD_NAME(self, x):
    """
    Args:
        x (torch.Tensor): Should be a 5D Tensor as [batch_size, channels, x_dims, y_dims, z_dims].
    Returns:
        x6 (torch.Tensor): Returns a 5D Output Tensor as [batch_size, n_classes, x_dims, y_dims, z_dims].
    """
    x = self.conv0_1x1(x)
    x1 = self.rn_0(x)
    x2 = self.ri_0(x1)
    x3 = self.ds_0(x2)
    x3 = self.ri_1(x3)
    x4 = self.ds_1(x3)
    x4 = self.ri_2(x4)
    x5 = self.ds_2(x4)
    x5 = self.ri_3(x5)
    x6 = self.ds_3(x5)
    x6 = self.ri_4(x6)
    x6 = self.us_3(x6)
    x6 = self.ri_5(torch.cat((x5, x6), dim=1))
    x6 = self.us_2(x6)
    x6 = self.ri_6(torch.cat((x4, x6), dim=1))
    x6 = self.us_1(x6)
    x6 = self.ri_7(torch.cat((x3, x6), dim=1))
    x6 = self.us_0(x6)
    x6 = self.ri_8(torch.cat((x2, x6), dim=1))
    x6 = self.conv9_1x1(x6)
    x6 = self.rn_10(torch.cat((x1, x6), dim=1))
    x6 = self.dropout(x6)
    if not self.final_convolution_layer is None:
        if self.final_convolution_layer == F.softmax:
            x6 = self.final_convolution_layer(x6, dim=1)
        else:
            x6 = self.final_convolution_layer(x6)
    return x6