missing batch_size error inconsistent with torch docs, batch_sampler arg

[tdhock]

?torch::dataloader says

batch_sampler: (Sampler, optional): like sampler, but returns a batch
          of indices at a time. Mutually exclusive with ‘batch_size’,
          ‘shuffle’, sampler, and ‘drop_last’. Custom samplers can be
          created with sampler().

So batch_size should not be provided at the same time at batch_sampler. However that contradicts the error message I get from mlr3torch when I run the code below:

batch_sampler_class <- torch::sampler(
  "BatchSampler",
  initialize = function(data_source) {
    self$data_source <- data_source
  },
  .iter = function() {
    batch_size <- 2
    indices <- 1:self$.length()
    batch_vec <- (indices-1) %/% batch_size
    batch_list <- rev(split(indices, batch_vec))
    count <- 0L
    function() {
      if (count < length(batch_list)) {
        count <<- count + 1L
        return(batch_list[[count]])
      }
      coro::exhausted()
    }
  },
  .length = function() {
    length(self$data_source)
  }
)
sonar_task <- mlr3::tsk("sonar")
sonar_ingress <- list(feat=mlr3torch::TorchIngressToken(
  features=sonar_task$col_roles$feature,
  batchgetter=mlr3torch::batchgetter_num))
target_batchgetter <- function(data){
  browser()
  torch::torch_tensor()
}
sonar_dataset <- mlr3torch::task_dataset(sonar_task, sonar_ingress, target_batchgetter)
batch_sampler_instance <- batch_sampler_class(sonar_dataset)
inst_learner <- mlr3torch::LearnerTorchMLP$new(task_type="classif")
inst_learner$param_set$set_values(
  epochs=10,
  batch_sampler=batch_sampler_class)
inst_learner$train(sonar_task)

In the code above I specified batch_sampler but I get the error message below:

> inst_learner$train(sonar_task)
Erreur dans .__ParamSet__get_values(self = self, private = private, super = super,  : 
  Missing required parameters: batch_size

The error message says I need to add batch_size parameter, but that contradicts the documentation in torch. @sebffischer Can this error message be removed please? It would be much less confusing for users if error messages in mlr3torch were consistent with the documented behavior in torch.

[sebffischer]

Thanks, will fix this!

[tdhock]

related to #420 and may be fixed by https://github.com/mlr-org/mlr3torch/pull/425