# source("utils.R")
# 
# test_model = function(occ = NULL, env, spatial=NULL, biotic = bioticStruct(), 
#                       iter = 1L, step_size = 10L, se=FALSE, family = stats::binomial(), context = "") {
#     sjSDM:::check_module()
#     device = is_gpu_available()
#     testthat::expect_error({model = sjSDM(!!occ, env=!!env, 
#                                           spatial = !!spatial, 
#                                           biotic = !!biotic,
#                                           iter = !!iter, 
#                                           step_size = !!step_size,
#                                           se = !!se,
#                                           family = !!family,
#                                           device=device,
#                                           sampling=10L, verbose = FALSE)}, NA)
#     testthat::expect_error({imp = importance(model)}, NA)
#     testthat::expect_error({plot(imp)}, NA)
# }
# 
# # 
# # testthat::test_that("sjSDM functionality", {
# #   skip_if_no_torch()
#   
#   library(sjSDM)
#   
#   sim = simulate_SDM(sites = 50L, species = 12, env = 3)
#   X1 = sim$env_weights
#   Y1 = sim$response
#   
#   
#   # iter, batch_size, se, link
#   # iter, batch_size, se, link
#   Funcs = list(
#     list(5, 2, FALSE, binomial("logit")),
#     list(5, 23, FALSE, poisson()),
#     list(5, 40, FALSE, gaussian()),
#     list(5, 40, FALSE, "nbinom")
#   )
#   testthat::test_that("sjSDM importance Func", {
#     testthat::skip_on_cran()
#     testthat::skip_on_ci()
#     skip_if_no_torch()
#     for(i in 1:length(Funcs)) {
#       test_model(Y1, env = linear(X1), iter = Funcs[[i]][[1]], step_size =  Funcs[[i]][[2]],  se = Funcs[[i]][[3]], family =  Funcs[[i]][[4]])
#     }
#   })
#   
#   
#   biotic = list(
#     bioticStruct(4L),
#     bioticStruct(4L, lambda = 0.1, alpha = 1.0)
#   )
#   testthat::test_that("sjSDM importance Biotic", {
#     testthat::skip_on_cran()
#     testthat::skip_on_ci()
#     skip_if_no_torch()
#     for(i in 1:length(biotic)) {
#       test_model(Y1, env=linear(X1), biotic = biotic[[i]])
#     }
#   })
#   
#   
#   envs = list(
#     linear(X1, ~0+X1:X2),
#     linear(X1, lambda = 0.1)
#   )
#   testthat::test_that("sjSDM importance env", {
#     testthat::skip_on_cran()
#     testthat::skip_on_ci()
#     skip_if_no_torch()
#     for(i in 1:length(envs)) {
#       test_model(Y1, env = envs[[i]])
#     }
#   })
#   
#   
#   
#   SP = matrix(rnorm(100), 50, 2)
#   Spatial = list(
#     linear(SP, ~0+X1:X2),
#     linear(SP, lambda = 0.1, alpha=0.0)
#   )
#   testthat::test_that("sjSDM importance Spatial", {
#     testthat::skip_on_cran()
#     testthat::skip_on_ci()
#     skip_if_no_torch()
#     for(i in 1:length(Spatial)) {
#       test_model(Y1, env = linear(X1), spatial = Spatial[[i]])
#     }
#   })
#   
#   
#   Spatial = list(
#     linear(SP, ~0+.)
#   )
#   
#   Env = list(
#     linear(X1, lambda = 0.1, alpha=0.5)
#   )
#   testthat::test_that("sjSDM importance Mix", {
#     testthat::skip_on_cran()
#     testthat::skip_on_ci()
#     skip_if_no_torch()
#     for(i in 1:length(Spatial)) {
#       test_model(Y1, env = Env[[i]], spatial = Spatial[[i]])
#     }
#   })
#   
# 
#