R/sagemakerruntime_interfaces.R

In paws.machine.learning: 'Amazon Web Services' Machine Learning Services

# This file is generated by make.paws. Please do not edit here.
#' @importFrom paws.common populate
#' @include sagemakerruntime_service.R
NULL

.sagemakerruntime$invoke_endpoint_input <- function(...) {
  args <- c(as.list(environment()), list(...))
  return(populate(args, .sagemakerruntime_shapes[["invoke_endpoint_input"]]))
}

.sagemakerruntime$invoke_endpoint_output <- function(...) {
  args <- c(as.list(environment()), list(...))
  return(populate(args, .sagemakerruntime_shapes[["invoke_endpoint_output"]]))
}

.sagemakerruntime$invoke_endpoint_async_input <- function(...) {
  args <- c(as.list(environment()), list(...))
  return(populate(args, .sagemakerruntime_shapes[["invoke_endpoint_async_input"]]))
}

.sagemakerruntime$invoke_endpoint_async_output <- function(...) {
  args <- c(as.list(environment()), list(...))
  return(populate(args, .sagemakerruntime_shapes[["invoke_endpoint_async_output"]]))
}

.sagemakerruntime$invoke_endpoint_with_response_stream_input <- function(...) {
  args <- c(as.list(environment()), list(...))
  return(populate(args, .sagemakerruntime_shapes[["invoke_endpoint_with_response_stream_input"]]))
}

.sagemakerruntime$invoke_endpoint_with_response_stream_output <- function(...) {
  args <- c(as.list(environment()), list(...))
  return(populate(args, .sagemakerruntime_shapes[["invoke_endpoint_with_response_stream_output"]]))
}