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summary of a trained `lbbnn_net` object. Includes the model type (input-skip or not), whether normalizing flows are used, module and sub-module structure, number of trainable parameters, and prior variance and inclusion probabilities for the weights.\n\n\n```r\n## S3 method for class 'lbbnn_net'\nprint(x, ...)\n```\n\n## Arguments\n\n- `x`: An object of class `lbbnn_net`.\n- `...`: Further arguments passed to or from other methods.\n\n## Returns\n\nInvisibly returns the input `x`.\n\n\n\n## Examples\n\n```r\nx<-torch::torch_randn(3,2)\nb <- torch::torch_rand(2)\ny <- torch::torch_matmul(x,b)\ntrain_data <- torch::tensor_dataset(x,y)\ntrain_loader <- torch::dataloader(train_data,batch_size = 3,shuffle=FALSE)\nproblem<-'regression'\nsizes <- c(2,1,1)\ninclusion_priors <-c(0.9,0.2)\ninclusion_inits <- matrix(rep(c(-10,10),2),nrow = 2,ncol = 2)\nstds <- c(1.0,1.0)\nmodel <- lbbnn_net(problem,sizes,inclusion_priors,stds,inclusion_inits,\nflow = FALSE, input_skip = 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