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#pragma once
#include <torch/csrc/Export.h>
#include <torch/data/samplers/base.h>
#include <torch/types.h>
#include <cstddef>
#include <vector>
namespace torch {
namespace serialize {
class OutputArchive;
class InputArchive;
} // namespace serialize
} // namespace torch
namespace torch {
namespace jit {
namespace mobile {
/// A lighter `Sampler` that returns indices randomly and cannot be
/// serialized.
class TORCH_API RandomSampler : public torch::data::samplers::Sampler<> {
public:
/// Constructs a `RandomSampler` with a size and dtype for the stored indices.
///
/// The constructor will eagerly allocate all required indices, which is the
/// sequence `0 ... size - 1`. `index_dtype` is the data type of the stored
/// indices. You can change it to influence memory usage.
explicit RandomSampler(int64_t size, Dtype index_dtype = torch::kInt64);
~RandomSampler() override;
/// Resets the `RandomSampler` to a new set of indices.
void reset(optional<size_t> new_size = nullopt) override;
/// Returns the next batch of indices.
optional<std::vector<size_t>> next(size_t batch_size) override;
/// Serializes the `RandomSampler` to the `archive`.
void save(serialize::OutputArchive& archive) const override;
/// Deserializes the `RandomSampler` from the `archive`.
void load(serialize::InputArchive& archive) override;
/// Returns the current index of the `RandomSampler`.
size_t index() const noexcept;
private:
at::Tensor indices_;
int64_t index_ = 0;
};
} // namespace mobile
} // namespace jit
} // namespace torch
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