Patch fix for cholesky in TF2 about greta HOT 4 CLOSED

    tf = function(dag) {
      # where to put it
      tfe <- dag$tf_environment
      # what to call the tensor object
      tf_name <- dag$tf_name(self)
      mode <- dag$how_to_define(self)

      # if sampling get the distribution constructor and sample this
      if (mode == "sampling") {
        tensor <- dag$draw_sample(self$distribution)
        if (has_representation(self, "cholesky")) {
          ## TF1/2
          ## This approach currently fails because of how we use representations
          ## within greta.
          # We will now error here since when sampling from a cholesky
          # represented variable, we don't really get consistent results
          cli::cli_warn(
            ## Could note that there are false positives?
            message = c(
              "We currently cannot use {.fun calculate} to sample a greta \\
              array with a cholesky factor, due to an internal issue with how \\
              greta handles cholesky representations.",
              "See issue here on github for more details:",
              "{.url https://github.com/greta-dev/greta/issues/593}"
            )
          )
          cholesky_tensor <- tf_chol(tensor)
          cholesky_tf_name <- dag$tf_name(self$representation$cholesky)
          assign(cholesky_tf_name, cholesky_tensor, envir = dag$tf_environment)

          # tf_name <- cholesky_tf_name
          # tensor <- cholesky_tensor
        }
      }

library(greta)
#> 
#> Attaching package: 'greta'
#> The following objects are masked from 'package:stats':
#> 
#>     binomial, cov2cor, poisson
#> The following objects are masked from 'package:base':
#> 
#>     %*%, apply, backsolve, beta, chol2inv, colMeans, colSums, diag,
#>     eigen, forwardsolve, gamma, identity, rowMeans, rowSums, sweep,
#>     tapply

# succeeds
sig <- lkj_correlation(2, dim = 2)
#> ℹ Initialising python and checking dependencies, this may take a moment.
#> ✔ Initialising python and checking dependencies ... done!
#> 
w <- wishart(5, sig)
m <- model(w)
draws <- mcmc(m, warmup = 0, n_samples = 5, verbose = FALSE)
draws
#> $`11`
#> Markov Chain Monte Carlo (MCMC) output:
#> Start = 1 
#> End = 5 
#> Thinning interval = 1 
#>       w[1,1]      w[2,1]      w[1,2]    w[2,2]
#> 1 0.06064959 -0.08968761 -0.08968761 0.2080036
#> 2 0.18153273 -0.16358580 -0.16358580 0.5022380
#> 3 0.11352640 -0.18384974 -0.18384974 0.5941550
#> 4 0.85345937 -0.98212422 -0.98212422 1.1355574
#> 5 0.77398713 -0.53847586 -0.53847586 0.5097147
#> 
#> $`12`
#> Markov Chain Monte Carlo (MCMC) output:
#> Start = 1 
#> End = 5 
#> Thinning interval = 1 
#>         w[1,1]      w[2,1]      w[1,2]      w[2,2]
#> 1 0.0007307899 0.001818318 0.001818318 0.004525567
#> 2 0.0007307899 0.001818318 0.001818318 0.004525567
#> 3 0.0007307899 0.001818318 0.001818318 0.004525567
#> 4 0.0007307899 0.001818318 0.001818318 0.004525567
#> 5 0.0007307899 0.001818318 0.001818318 0.004525567
#> 
#> $`13`
#> Markov Chain Monte Carlo (MCMC) output:
#> Start = 1 
#> End = 5 
#> Thinning interval = 1 
#>       w[1,1]      w[2,1]      w[1,2]    w[2,2]
#> 1 0.14602971 -0.13032268 -0.13032268 0.3595358
#> 2 0.08595726 -0.06137547 -0.06137547 0.8014967
#> 3 0.30209596 -0.02779237 -0.02779237 0.7412944
#> 4 0.02448499 -0.04408638 -0.04408638 0.8345146
#> 5 0.12286190  0.05137151  0.05137151 1.4068696
#> 
#> $`14`
#> Markov Chain Monte Carlo (MCMC) output:
#> Start = 1 
#> End = 5 
#> Thinning interval = 1 
#>      w[1,1]      w[2,1]      w[1,2]    w[2,2]
#> 1 0.1987510 -0.03785009 -0.03785009 0.1985830
#> 2 0.1984730 -0.03523137 -0.03523137 0.2151315
#> 3 0.7619678 -0.42175497 -0.42175497 0.2995763
#> 4 0.8598383 -0.57636239 -0.57636239 0.5204147
#> 5 1.4662869 -0.60184908 -0.60184908 0.9378646
#> 
#> attr(,"class")
#> [1] "greta_mcmc_list" "mcmc.list"      
#> attr(,"model_info")
#> attr(,"model_info")$raw_draws
#> $`11`
#> Markov Chain Monte Carlo (MCMC) output:
#> Start = 1 
#> End = 5 
#> Thinning interval = 1 
#>           1         2          3          4
#> 1 0.4324554 0.2462714 -0.3641820 0.27454514
#> 2 0.5676601 0.4260666 -0.3839442 0.59567174
#> 3 0.7014721 0.3369368 -0.5456505 0.54444511
#> 4 1.1698393 0.9238286 -1.0631022 0.07328834
#> 5 1.0902837 0.8797654 -0.6120676 0.36754319
#> 
#> $`12`
#> Markov Chain Monte Carlo (MCMC) output:
#> Start = 1 
#> End = 5 
#> Thinning interval = 1 
#>             1          2          3           4
#> 1 -0.03417148 0.02703313 0.06726259 0.001144732
#> 2 -0.03417148 0.02703313 0.06726259 0.001144732
#> 3 -0.03417148 0.02703313 0.06726259 0.001144732
#> 4 -0.03417148 0.02703313 0.06726259 0.001144732
#> 5 -0.03417148 0.02703313 0.06726259 0.001144732
#> 
#> $`13`
#> Markov Chain Monte Carlo (MCMC) output:
#> Start = 1 
#> End = 5 
#> Thinning interval = 1 
#>           1         2           3         4
#> 1 0.1643967 0.3821383 -0.34103534 0.4931842
#> 2 0.2189142 0.2931847 -0.20934064 0.8704443
#> 3 0.6258184 0.5496326 -0.05056536 0.8594984
#> 4 0.6418233 0.1564768 -0.28174385 0.8689851
#> 5 1.0310803 0.3505166  0.14655942 1.1770259
#> 
#> $`14`
#> Markov Chain Monte Carlo (MCMC) output:
#> Start = 1 
#> End = 5 
#> Thinning interval = 1 
#>             1         2           3         4
#> 1 -0.19703020 0.4458150 -0.08490090 0.4374641
#> 2  0.17551313 0.4455031 -0.07908221 0.4570312
#> 3  0.32429059 0.8729076 -0.48316104 0.2571609
#> 4  0.13162990 0.9272746 -0.62156600 0.3661563
#> 5 -0.02499826 1.2109033 -0.49702488 0.8311624
#> 
#> attr(,"class")
#> [1] "mcmc.list"
#> 
#> attr(,"model_info")$samplers
#> attr(,"model_info")$samplers$`1`
#> Error in vapply(x, format, "", big.mark = big.mark, big.interval = big.interval, : values must be length 1,
#>  but FUN(X[[4]]) result is length 4

# fails
x <- wishart(df = 4, Sigma = diag(3))
chol_x <- chol(x)
calc_chol <- calculate(x, chol_x, nsim = 1)
#> Warning: We currently cannot use `calculate()` to sample a greta array with a cholesky
#> factor, due to an internal issue with how greta handles cholesky
#> representations.
#> See issue here on github for more details:
#> <https://github.com/greta-dev/greta/issues/593>
calc_chol
#> $x
#> , , 1
#> 
#>          [,1]     [,2]      [,3]
#> [1,] 12.53445 3.035458 -2.170019
#> 
#> , , 2
#> 
#>          [,1]     [,2]        [,3]
#> [1,] 3.035458 4.972589 -0.09387038
#> 
#> , , 3
#> 
#>           [,1]        [,2]     [,3]
#> [1,] -2.170019 -0.09387038 1.807308
#> 
#> 
#> $chol_x
#> , , 1
#> 
#>      [,1] [,2] [,3]
#> [1,]    1    1    1
#> 
#> , , 2
#> 
#>      [,1] [,2] [,3]
#> [1,]    1    1    1
#> 
#> , , 3
#> 
#>      [,1] [,2] [,3]
#> [1,]    1    1    1

library(greta)
#> 
#> Attaching package: 'greta'
#> The following objects are masked from 'package:stats':
#> 
#>     binomial, cov2cor, poisson
#> The following objects are masked from 'package:base':
#> 
#>     %*%, apply, backsolve, beta, chol2inv, colMeans, colSums, diag,
#>     eigen, forwardsolve, gamma, identity, rowMeans, rowSums, sweep,
#>     tapply

x <- wishart(df = 4, Sigma = diag(3))
#> ℹ Initialising python and checking dependencies, this may take a moment.
#> ✔ Initialising python and checking dependencies ... done!
#> 
x
#> greta array (operation following a wishart distribution)
#> 
#>      [,1] [,2] [,3]
#> [1,]  ?    ?    ?  
#> [2,]  ?    ?    ?  
#> [3,]  ?    ?    ?

pre_mcmc <- calculate(x, nsim = 1)
pre_mcmc
#> $x
#> , , 1
#> 
#>          [,1]      [,2]     [,3]
#> [1,] 5.055917 0.6333886 3.368602
#> 
#> , , 2
#> 
#>           [,1]     [,2]      [,3]
#> [1,] 0.6333886 1.445194 0.9147159
#> 
#> , , 3
#> 
#>          [,1]      [,2]     [,3]
#> [1,] 3.368602 0.9147159 4.388693

chol_x <- chol(x)
calculate(chol_x, nsim = 1)
#> Warning: Cannot use `calculate()` to sample a cholesky factor of a greta array
#> E.g., `x_chol <- chol(wishart(df = 4, Sigma = diag(3)))`
#> `calculate(x_chol)`
#> This is due to an internal issue with how greta handles cholesky
#> representations.
#> See issue here on github for more details:
#> <https://github.com/greta-dev/greta/issues/593>
#> $chol_x
#> , , 1
#> 
#>      [,1] [,2] [,3]
#> [1,]    1    1    1
#> 
#> , , 2
#> 
#>      [,1] [,2] [,3]
#> [1,]    1    1    1
#> 
#> , , 3
#> 
#>      [,1] [,2] [,3]
#> [1,]    1    1    1

calculate(x, nsim = 1)
#> Warning: Cannot use `calculate()` to sample a cholesky factor of a greta array
#> E.g., `x_chol <- chol(wishart(df = 4, Sigma = diag(3)))`
#> `calculate(x_chol)`
#> This is due to an internal issue with how greta handles cholesky
#> representations.
#> See issue here on github for more details:
#> <https://github.com/greta-dev/greta/issues/593>
#> $x
#> , , 1
#> 
#>          [,1]       [,2]      [,3]
#> [1,] 5.687854 -0.3524896 -1.104498
#> 
#> , , 2
#> 
#>            [,1]     [,2]   [,3]
#> [1,] -0.3524896 1.861157 -1.468
#> 
#> , , 3
#> 
#>           [,1]   [,2]     [,3]
#> [1,] -1.104498 -1.468 2.467221

chol_x_p1 <- chol_x + 1
calculate(x, chol_x, chol_x_p1, nsim = 1)
#> Warning: Cannot use `calculate()` to sample a cholesky factor of a greta array
#> E.g., `x_chol <- chol(wishart(df = 4, Sigma = diag(3)))`
#> `calculate(x_chol)`
#> This is due to an internal issue with how greta handles cholesky
#> representations.
#> See issue here on github for more details:
#> <https://github.com/greta-dev/greta/issues/593>
#> $x
#> , , 1
#> 
#>          [,1]     [,2]      [,3]
#> [1,] 1.883891 2.321163 0.5246651
#> 
#> , , 2
#> 
#>          [,1]     [,2]      [,3]
#> [1,] 2.321163 4.046714 0.5877278
#> 
#> , , 3
#> 
#>           [,1]      [,2]      [,3]
#> [1,] 0.5246651 0.5877278 0.6911986
#> 
#> 
#> $chol_x
#> , , 1
#> 
#>      [,1] [,2] [,3]
#> [1,]    1    1    1
#> 
#> , , 2
#> 
#>      [,1] [,2] [,3]
#> [1,]    1    1    1
#> 
#> , , 3
#> 
#>      [,1] [,2] [,3]
#> [1,]    1    1    1
#> 
#> 
#> $chol_x_p1
#> , , 1
#> 
#>      [,1] [,2] [,3]
#> [1,]    2    2    2
#> 
#> , , 2
#> 
#>      [,1] [,2] [,3]
#> [1,]    2    2    2
#> 
#> , , 3
#> 
#>      [,1] [,2] [,3]
#> [1,]    2    2    2

calculate(x, chol_x, nsim = 1)
#> Warning: Cannot use `calculate()` to sample a cholesky factor of a greta array
#> E.g., `x_chol <- chol(wishart(df = 4, Sigma = diag(3)))`
#> `calculate(x_chol)`
#> This is due to an internal issue with how greta handles cholesky
#> representations.
#> See issue here on github for more details:
#> <https://github.com/greta-dev/greta/issues/593>
#> $x
#> , , 1
#> 
#>          [,1]     [,2]      [,3]
#> [1,] 11.52874 1.026355 -1.737126
#> 
#> , , 2
#> 
#>          [,1]    [,2]      [,3]
#> [1,] 1.026355 1.71024 0.5303688
#> 
#> , , 3
#> 
#>           [,1]      [,2]     [,3]
#> [1,] -1.737126 0.5303688 1.886979
#> 
#> 
#> $chol_x
#> , , 1
#> 
#>      [,1] [,2] [,3]
#> [1,]    1    1    1
#> 
#> , , 2
#> 
#>      [,1] [,2] [,3]
#> [1,]    1    1    1
#> 
#> , , 3
#> 
#>      [,1] [,2] [,3]
#> [1,]    1    1    1

m <- model(x)
draws <- mcmc(m, warmup = 1, n_samples = 1)
#> running 4 chains simultaneously on up to 8 CPU cores
#> 
#> warmup 0/1 | eta: ?s sampling 0/1 | eta: ?s

post_mcmc <- calculate(x, nsim = 1)
#> Warning: Cannot use `calculate()` to sample a cholesky factor of a greta array
#> E.g., `x_chol <- chol(wishart(df = 4, Sigma = diag(3)))`
#> `calculate(x_chol)`
#> This is due to an internal issue with how greta handles cholesky
#> representations.
#> See issue here on github for more details:
#> <https://github.com/greta-dev/greta/issues/593>
post_mcmc
#> $x
#> , , 1
#> 
#>          [,1] [,2] [,3]
#> [1,] 1.048605    0    0
#> 
#> , , 2
#> 
#>          [,1]     [,2] [,3]
#> [1,] 1.126161 2.339339    0
#> 
#> , , 3
#> 
#>           [,1]     [,2]     [,3]
#> [1,] 0.5542009 1.327417 2.427972