Scaffold R wrappers for Python functions

scaffold_py_function_wrapper(
python_function,
r_function = NULL,
file_name = NULL
)

## Arguments

python_function Fully qualified name of Python function or class constructor (e.g. tf$nn$top_k) Name of R function to generate (defaults to name of Python function if not specified) The file name to write the generated wrapper function to. If NULL, the generated wrapper will only be printed out in the console.

## Note

The generated wrapper will often require additional editing (e.g. to convert Python list literals in the docs to R lists, to massage R numeric values to Python integers via as.integer where required, etc.) so is really intended as an starting point for an R wrapper rather than a wrapper that can be used without modification.

## Examples

# \donttest{

library(scaffolder)
library(tensorflow)

scaffold_py_function_wrapper("tf$nn$top_k")#> #' @title top_k
#> #'
#> #' @description Finds values and indices of the k largest entries for the last dimension.
#> #'
#> #' @details If the input is a vector (rank=1), finds the k largest entries in the vector
#> #' and outputs their values and indices as vectors. Thus values[j] is the
#> #' j-th largest entry in input, and its index is indices[j]. For matrices (resp. higher rank input), computes the top k entries in each
#> #' row (resp. vector along the last dimension). Thus, values.shape = indices.shape = input.shape[:-1] + [k] If two elements are equal, the lower-index element appears first.
#> #'
#> #' @param input 1-D or higher Tensor with last dimension at least k.
#> #' @param k 0-D int32 Tensor. Number of top elements to look for along the last dimension (along each row for matrices).
#> #' @param sorted If true the resulting k elements will be sorted by the values in descending order.
#> #' @param name Optional name for the operation.
#> #'
#> #' @return values: The k largest elements along each last dimensional slice. indices: The indices of values within the last dimension of input.
#> #'
#> #' @export
#> top_k <- function(input, k = 1L, sorted = TRUE, name = NULL) {
#>
#>   python_function_result <- tf$nn$top_k(
#>     input = input,
#>     k = k,
#>     sorted = sorted,
#>     name = name
#>   )
#>
#> }# }