pytorch

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#include <torch/csrc/jit/mobile/train/sequential.h>
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#include <torch/types.h>
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#include <algorithm>
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#include <cstddef>
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#include <vector>
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namespace torch {
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namespace jit {
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namespace mobile {
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SequentialSampler::SequentialSampler(size_t size) : size_(size) {}
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void SequentialSampler::reset(optional<size_t> new_size) {
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  if (new_size.has_value()) {
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    size_ = *new_size;
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  }
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  index_ = 0;
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}
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optional<std::vector<size_t>> SequentialSampler::next(size_t batch_size) {
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  const auto remaining_indices = size_ - index_;
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  if (remaining_indices == 0) {
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    return nullopt;
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  }
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  std::vector<size_t> index_batch(std::min(batch_size, remaining_indices));
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  for (auto& i : index_batch) {
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    i = index_++;
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  }
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  return index_batch;
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}
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void SequentialSampler::save(serialize::OutputArchive& archive) const {
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  TORCH_CHECK(
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      false, "Serialization of SequentialSampler not supported on mobile.");
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}
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void SequentialSampler::load(serialize::InputArchive& archive) {
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  TORCH_CHECK(
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      false, "Serialization of SequentialSampler not supported on mobile.");
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}
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size_t SequentialSampler::index() const noexcept {
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  return index_;
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}
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} // namespace mobile
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} // namespace jit
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} // namespace torch
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