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`)
r_function	Name of R function to generate (defaults to name of Python function if not specified)
file_name	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
#>   )
#> 
#> }
# }