#if !defined(TORCH_STABLE_ONLY) && !defined(TORCH_TARGET_VERSION)
#pragma once

#include <torch/nn/options/instancenorm.h>

namespace torch::nn::functional {

#ifndef DOXYGEN_SHOULD_SKIP_THIS
namespace detail {
inline Tensor instance_norm(
    const Tensor& input,
    const Tensor& running_mean,
    const Tensor& running_var,
    const Tensor& weight,
    const Tensor& bias,
    bool use_input_stats,
    double momentum,
    double eps) {
  return torch::instance_norm(
      input,
      weight,
      bias,
      running_mean,
      running_var,
      use_input_stats,
      momentum,
      eps,
      at::globalContext().userEnabledCuDNN());
}
} // namespace detail
#endif /* DOXYGEN_SHOULD_SKIP_THIS */

/// See
/// https://pytorch.org/docs/main/nn.functional.html#torch.nn.functional.instance_norm
/// about the exact behavior of this functional.
///
/// See the documentation for `torch::nn::functional::InstanceNormFuncOptions`
/// class to learn what optional arguments are supported for this functional.
///
/// Example:
/// ```
/// namespace F = torch::nn::functional;
/// F::instance_norm(input,
/// F::InstanceNormFuncOptions().running_mean(mean).running_var(variance).weight(weight).bias(bias).momentum(0.1).eps(1e-5));
/// ```
inline Tensor instance_norm(
    const Tensor& input,
    const InstanceNormFuncOptions& options = {}) {
  return detail::instance_norm(
      input,
      options.running_mean(),
      options.running_var(),
      options.weight(),
      options.bias(),
      options.use_input_stats(),
      options.momentum(),
      options.eps());
}

} // namespace torch::nn::functional

#else
#error "This file should not be included when either TORCH_STABLE_ONLY or TORCH_TARGET_VERSION is defined."
#endif  // !defined(TORCH_STABLE_ONLY) && !defined(TORCH_TARGET_VERSION)
