* using log directory ‘/data/blackswan/ripley/R/packages/tests-devel/downscaledl.Rcheck’
* using R Under development (unstable) (2023-03-24 r84037)
* using platform: x86_64-pc-linux-gnu (64-bit)
* R was compiled by
    gcc (GCC) 11.3.1 20220421 (Red Hat 11.3.1-2)
    GNU Fortran (GCC) 11.3.1 20220421 (Red Hat 11.3.1-2)
* running under: Fedora 34 (Workstation Edition)
* using session charset: UTF-8
* checking for file ‘downscaledl/DESCRIPTION’ ... OK
* checking extension type ... Package
* this is package ‘downscaledl’ version ‘1.0’
* package encoding: UTF-8
* checking package namespace information ... OK
* checking package dependencies ... OK
* checking if this is a source package ... OK
* checking if there is a namespace ... OK
* checking for executable files ... OK
* checking for hidden files and directories ... OK
* checking for portable file names ... OK
* checking for sufficient/correct file permissions ... OK
* checking whether package ‘downscaledl’ can be installed ... [21s/21s] OK
* used C++ compiler: ‘g++ (GCC) 11.3.1 20220421 (Red Hat 11.3.1-2)’
* checking C++ specification ... NOTE
  Specified C++11: please drop specification unless essential
* checking package directory ... OK
* checking DESCRIPTION meta-information ... OK
* checking top-level files ... OK
* checking for left-over files ... OK
* checking index information ... OK
* checking package subdirectories ... OK
* checking R files for non-ASCII characters ... OK
* checking R files for syntax errors ... OK
* checking whether the package can be loaded ... OK
* checking whether the package can be loaded with stated dependencies ... OK
* checking whether the package can be unloaded cleanly ... OK
* checking whether the namespace can be loaded with stated dependencies ... OK
* checking whether the namespace can be unloaded cleanly ... OK
* checking loading without being on the library search path ... OK
* checking dependencies in R code ... NOTE
Namespaces in Imports field not imported from:
  ‘dplyr’ ‘parallel’ ‘rgdal’ ‘sp’ ‘tensorflow’
  All declared Imports should be used.
* checking S3 generic/method consistency ... OK
* checking replacement functions ... OK
* checking foreign function calls ... OK
* checking R code for possible problems ... OK
* checking Rd files ... OK
* checking Rd metadata ... OK
* checking Rd line widths ... OK
* checking Rd cross-references ... OK
* checking for missing documentation entries ... OK
* checking for code/documentation mismatches ... OK
* checking Rd \usage sections ... OK
* checking Rd contents ... OK
* checking for unstated dependencies in examples ... OK
* checking LazyData ... NOTE
  'LazyData' is specified without a 'data' directory
* checking line endings in C/C++/Fortran sources/headers ... OK
* checking line endings in Makefiles ... OK
* checking compilation flags in Makevars ... OK
* checking for GNU extensions in Makefiles ... OK
* checking for portable use of $(BLAS_LIBS) and $(LAPACK_LIBS) ... OK
* checking use of PKG_*FLAGS in Makefiles ... OK
* checking use of SHLIB_OPENMP_*FLAGS in Makefiles ... OK
* checking compilation flags used ... OK
* checking compiled code ... OK
* checking examples ... OK
* checking examples with --run-donttest ... ERROR
Running examples in ‘downscaledl-Ex.R’ failed
The error most likely occurred in:

> ### Name: AutoEncoderModel
> ### Title: AutoEncoderModel
> ### Aliases: AutoEncoderModel
> 
> ### ** Examples
> 
>   # This is an example to simulate a dataset to demonstrate use of autoencoder 
>   
>   #Set the sample size as 1000 for the simulated dataset 
>   n=1000
>   
>   #Get the simulated data using random functions 
>   dataDf=data.frame(id=c(1:n),x1=runif(n),x2=rnorm(n,100,10),
+              x3=runif(n,100,200),x4=rnorm(n,1000,30))
>   
>   #Set the proportion of the test samples  
>   testProp=0.1
>   ntest=as.integer(n*testProp)
>   ntrain=n-ntest 
>   
>   #Obtain the index for the training and testing samples 
>   index_train=sample(c(1:n),ntrain)
>   index_test=setdiff(c(1:n),index_train)
> 
>   #Obtain y as analytic solution for x plus random noise 
>   dataDf$y=sqrt(dataDf$x1)+dataDf$x2^0.3+log(dataDf$x3)+dataDf$x4^2+rnorm(n)
> 
>   #Scale the dataset 
>   scalev = scale(dataDf[,c(2:6)]) 
>   col_means = attr(scalev, "scaled:center") 
>   col_stddevs = attr(scalev, "scaled:scale")
>   
>   ## No test: 
>   #Set the early stopping and learning rate adjustement functions 
>   early_stopping = keras::callback_early_stopping(monitor ='loss', min_delta=0.000001)
Error: Valid installation of TensorFlow not found.

Python environments searched for 'tensorflow' package:
 /usr/bin/python3.9
 /usr/bin/python3.9
 /usr/bin/python3.9

Python exception encountered:
 Traceback (most recent call last):
  File "/data/blackswan/ripley/R/R-devel/site-library/reticulate/python/rpytools/loader.py", line 119, in _find_and_load_hook
    return _run_hook(name, _hook)
  File "/data/blackswan/ripley/R/R-devel/site-library/reticulate/python/rpytools/loader.py", line 93, in _run_hook
    module = hook()
  File "/data/blackswan/ripley/R/R-devel/site-library/reticulate/python/rpytools/loader.py", line 117, in _hook
    return _find_and_load(name, import_)
ModuleNotFoundError: No module named 'tensorflow'


You can install TensorFlow using the install_tensorflow() function.
Execution halted
* checking PDF version of manual ... OK
* checking for non-standard things in the check directory ... OK
* checking for detritus in the temp directory ... OK
* checking for new files in some other directories ... OK
* DONE

Status: 1 ERROR, 3 NOTEs
See
  ‘/data/blackswan/ripley/R/packages/tests-devel/downscaledl.Rcheck/00check.log’
for details.

Command exited with non-zero status 1
Time 0:58.73, 51.42 + 11.87