#pragma once

// @generated by torchgen/gen.py from Operator.h

#include <string_view>
#include <tuple>
#include <vector>

// Forward declarations of any types needed in the operator signatures.
// We can't directly include these classes because it will cause circular include dependencies.
// This file is included by TensorBody.h, which defines the Tensor class.
#include <ATen/core/ATen_fwd.h>

namespace at {
namespace _ops {


struct TORCH_API rnn_tanh_input {
  using schema = ::std::tuple<at::Tensor,at::Tensor> (const at::Tensor &, const at::Tensor &, at::TensorList, bool, int64_t, double, bool, bool, bool);
  using ptr_schema = schema*;
  // See Note [static constexpr char* members for windows NVCC]
  static constexpr const char* name = "aten::rnn_tanh";
  static constexpr const char* overload_name = "input";
  static constexpr const char* schema_str = "rnn_tanh.input(Tensor input, Tensor hx, Tensor[] params, bool has_biases, int num_layers, float dropout, bool train, bool bidirectional, bool batch_first) -> (Tensor, Tensor)";
  static ::std::tuple<at::Tensor,at::Tensor> call(const at::Tensor & input, const at::Tensor & hx, at::TensorList params, bool has_biases, int64_t num_layers, double dropout, bool train, bool bidirectional, bool batch_first);
  static ::std::tuple<at::Tensor,at::Tensor> redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, const at::Tensor & hx, at::TensorList params, bool has_biases, int64_t num_layers, double dropout, bool train, bool bidirectional, bool batch_first);
};

struct TORCH_API rnn_tanh_data {
  using schema = ::std::tuple<at::Tensor,at::Tensor> (const at::Tensor &, const at::Tensor &, const at::Tensor &, at::TensorList, bool, int64_t, double, bool, bool);
  using ptr_schema = schema*;
  // See Note [static constexpr char* members for windows NVCC]
  static constexpr const char* name = "aten::rnn_tanh";
  static constexpr const char* overload_name = "data";
  static constexpr const char* schema_str = "rnn_tanh.data(Tensor data, Tensor batch_sizes, Tensor hx, Tensor[] params, bool has_biases, int num_layers, float dropout, bool train, bool bidirectional) -> (Tensor, Tensor)";
  static ::std::tuple<at::Tensor,at::Tensor> call(const at::Tensor & data, const at::Tensor & batch_sizes, const at::Tensor & hx, at::TensorList params, bool has_biases, int64_t num_layers, double dropout, bool train, bool bidirectional);
  static ::std::tuple<at::Tensor,at::Tensor> redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & data, const at::Tensor & batch_sizes, const at::Tensor & hx, at::TensorList params, bool has_biases, int64_t num_layers, double dropout, bool train, bool bidirectional);
};

}} // namespace at::_ops
