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# Source code for torch.nn.modules.rnn

    import math
    import warnings
    import numbers
    import weakref
    from typing import List, Tuple, Optional, overload

    import torch
    from torch import Tensor
    from .module import Module
    from ..parameter import Parameter
    from ..utils.rnn import PackedSequence
    from .. import init
    from ... import _VF

    __all__ = ['RNNBase', 'RNN', 'LSTM', 'GRU', 'RNNCellBase', 'RNNCell', 'LSTMCell', 'GRUCell']

    _rnn_impls = {
        'RNN_TANH': _VF.rnn_tanh,
        'RNN_RELU': _VF.rnn_relu,
    }

    def _apply_permutation(tensor: Tensor, permutation: Tensor, dim: int = 1) -> Tensor:
        return tensor.index_select(dim, permutation)

    def apply_permutation(tensor: Tensor, permutation: Tensor, dim: int = 1) -> Tensor:
        warnings.warn("apply_permutation is deprecated, please use tensor.index_select(dim, permutation) instead")
        return _apply_permutation(tensor, permutation, dim)

    class RNNBase(Module):
        r"""Base class for RNN modules (RNN, LSTM, GRU).

        Implements aspects of RNNs shared by the RNN, LSTM, and GRU classes, such as module initialization
        and utility methods for parameter storage management.

        .. note::
            The forward method is not implemented by the RNNBase class.

        .. note::
            LSTM and GRU classes override some methods implemented by RNNBase.
        """
        __constants__ = ['mode', 'input_size', 'hidden_size', 'num_layers', 'bias',
                         'batch_first', 'dropout', 'bidirectional', 'proj_size']
        __jit_unused_properties__ = ['all_weights']

        mode: str
        input_size: int
        hidden_size: int
        num_layers: int
        bias: bool
        batch_first: bool
        dropout: float
        bidirectional: bool
        proj_size: int

        def __init__(self, mode: str, input_size: int, hidden_size: int,
                     num_layers: int = 1, bias: bool = True, batch_first: bool = False,
                     dropout: float = 0., bidirectional: bool = False, proj_size: int = 0,
                     device=None, dtype=None) -> None:
            factory_kwargs = {'device': device, 'dtype': dtype}
            super().__init__()
            self.mode = mode
            self.input_size = input_size
            self.hidden_size = hidden_size
            self.num_layers = num_layers
            self.bias = bias
            self.batch_first = batch_first
            self.dropout = float(dropout)
            self.bidirectional = bidirectional
            self.proj_size = proj_size
            self._flat_weight_refs: List[Optional[weakref.ReferenceType[Parameter]]] = []
            num_directions = 2 if bidirectional else 1

            if not isinstance(dropout, numbers.Number) or not 0 <= dropout <= 1 or \
                    isinstance(dropout, bool):
                raise ValueError("dropout should be a number in range [0, 1] "
                                 "representing the probability of an element being "
                                 "zeroed")
            if dropout > 0 and num_layers == 1:
                warnings.warn("dropout option adds dropout after all but last "
                              "recurrent layer, so non-zero dropout expects "
                              f"num_layers greater than 1, but got dropout={dropout} and "
                              f"num_layers={num_layers}")

            if not isinstance(hidden_size, int):
                raise TypeError(f"hidden_size should be of type int, got: {type(hidden_size).__name__}")
            if hidden_size <= 0:
                raise ValueError("hidden_size must be greater than zero")
            if proj_size < 0:
                raise ValueError("proj_size should be a positive integer or zero to disable projections")
            if proj_size >= hidden_size:
                raise ValueError("proj_size has to be smaller than hidden_size")

            if mode == 'LSTM':
                gate_size = 4 * hidden_size
            elif mode == 'GRU':
                gate_size = 3 * hidden_size
            elif mode == 'RNN_TANH':
                gate_size = hidden_size
            elif mode == 'RNN_RELU':
                gate_size = hidden_size
            else:
                raise ValueError("Unrecognized RNN mode: " + mode)

            self._flat_weights_names = []
            self._all_weights = []
            for layer in range(num_layers):
                for direction in range(num_directions):
                    real_hidden_size = proj_size if proj_size > 0 else hidden_size
                    layer_input_size = input_size if layer == 0 else real_hidden_size * num_directions

                    w_ih = Parameter(torch.empty((gate_size, layer_input_size), **factory_kwargs))
                    w_hh = Parameter(torch.empty((gate_size, real_hidden_size), **factory_kwargs))
                    b_ih = Parameter(torch.empty(gate_size, **factory_kwargs))
                    # Second bias vector included for CuDNN compatibility. Only one
                    # bias vector is needed in standard definition.
                    b_hh = Parameter(torch.empty(gate_size, **factory_kwargs))
                    layer_params: Tuple[Tensor, ...] = ()
                    if self.proj_size == 0:
                        if bias:
                            layer_params = (w_ih, w_hh, b_ih, b_hh)
                        else:
                            layer_params = (w_ih, w_hh)
                    else:
                        w_hr = Parameter(torch.empty((proj_size, hidden_size), **factory_kwargs))
                        if bias:
                            layer_params = (w_ih, w_hh, b_ih, b_hh, w_hr)
                        else:
                            layer_params = (w_ih, w_hh, w_hr)

                    suffix = '_reverse' if direction == 1 else ''
                    param_names = ['weight_ih_l{}{}', 'weight_hh_l{}{}']
                    if bias:
                        param_names += ['bias_ih_l{}{}', 'bias_hh_l{}{}']
                    if self.proj_size > 0:
                        param_names += ['weight_hr_l{}{}']
                    param_names = [x.format(layer, suffix) for x in param_names]

                    for name, param in zip(param_names, layer_params):
                        setattr(self, name, param)
                    self._flat_weights_names.extend(param_names)
                    self._all_weights.append(param_names)

            self._init_flat_weights()

            self.reset_parameters()

        def _init_flat_weights(self):
            self._flat_weights = [getattr(self, wn) if hasattr(self, wn) else None
                                  for wn in self._flat_weights_names]
            self._flat_weight_refs = [weakref.ref(w) if w is not None else None
                                      for w in self._flat_weights]
            self.flatten_parameters()

        def __setattr__(self, attr, value):
            if hasattr(self, "_flat_weights_names") and attr in self._flat_weights_names:
                # keep self._flat_weights up to date if you do self.weight = ...
                idx = self._flat_weights_names.index(attr)
                self._flat_weights[idx] = value
            super().__setattr__(attr, value)

        def flatten_parameters(self) -> None:
            """Resets parameter data pointer so that they can use faster code paths.

            Right now, this works only if the module is on the GPU and cuDNN is enabled.
            Otherwise, it's a no-op.
            """
            # Short-circuits if _flat_weights is only partially instantiated
            if len(self._flat_weights) != len(self._flat_weights_names):
                return

            for w in self._flat_weights:
                if not isinstance(w, Tensor):
                    return
            # Short-circuits if any tensor in self._flat_weights is not acceptable to cuDNN
            # or the tensors in _flat_weights are of different dtypes

            first_fw = self._flat_weights[0]
            dtype = first_fw.dtype
            for fw in self._flat_weights:
                if (not isinstance(fw.data, Tensor) or not (fw.data.dtype == dtype) or
                        not fw.data.is_cuda or
                        not torch.backends.cudnn.is_acceptable(fw.data)):
                    return

            # If any parameters alias, we fall back to the slower, copying code path. This is
            # a sufficient check, because overlapping parameter buffers that don't completely
            # alias would break the assumptions of the uniqueness check in
            # Module.named_parameters().
            unique_data_ptrs = {p.data_ptr() for p in self._flat_weights}
            if len(unique_data_ptrs) != len(self._flat_weights):
                return

            with torch.cuda.device_of(first_fw):
                import torch.backends.cudnn.rnn as rnn

                # Note: no_grad() is necessary since _cudnn_rnn_flatten_weight is
                # an inplace operation on self._flat_weights
                with torch.no_grad():
                    if torch._use_cudnn_rnn_flatten_weight():
                        num_weights = 4 if self.bias else 2
                        if self.proj_size > 0:
                            num_weights += 1
                        torch._cudnn_rnn_flatten_weight(
                            self._flat_weights, num_weights,
                            self.input_size, rnn.get_cudnn_mode(self.mode),
                            self.hidden_size, self.proj_size, self.num_layers,
                            self.batch_first, bool(self.bidirectional))

        def _apply(self, fn, recurse=True):
            ret = super()._apply(fn, recurse)

            # Resets _flat_weights
            # Note: be v. careful before removing this, as 3rd party device types
            # likely rely on this behavior to properly .to() modules like LSTM.
            self._init_flat_weights()

            return ret

        def reset_parameters(self) -> None:
            stdv = 1.0 / math.sqrt(self.hidden_size) if self.hidden_size > 0 else 0
            for weight in self.parameters():
                init.uniform_(weight, -stdv, stdv)

        def check_input(self, input: Tensor, batch_sizes: Optional[Tensor]) -> None:
            if not torch.jit.is_scripting():
                if input.dtype != self._flat_weights[0].dtype and not torch._C._is_any_autocast_enabled():
                    raise ValueError(f'input must have the type {self._flat_weights[0].dtype}, got type {input.dtype}')
            expected_input_dim = 2 if batch_sizes is not None else 3
            if input.dim() != expected_input_dim:
                raise RuntimeError(
                    f'input must have {expected_input_dim} dimensions, got {input.dim()}')
            if self.input_size != input.size(-1):
                raise RuntimeError(
                    f'input.size(-1) must be equal to input_size. Expected {self.input_size}, got {input.size(-1)}')

        def get_expected_hidden_size(self, input: Tensor, batch_sizes: Optional[Tensor]) -> Tuple[int, int, int]:
            if batch_sizes is not None:
                mini_batch = int(batch_sizes[0])
            else:
                mini_batch = input.size(0) if self.batch_first else input.size(1)
            num_directions = 2 if self.bidirectional else 1
            if self.proj_size > 0:
                expected_hidden_size = (self.num_layers * num_directions,
                                        mini_batch, self.proj_size)
            else:
                expected_hidden_size = (self.num_layers * num_directions,
                                        mini_batch, self.hidden_size)
            return expected_hidden_size

        def check_hidden_size(self, hx: Tensor, expected_hidden_size: Tuple[int, int, int],
                              msg: str = 'Expected hidden size {}, got {}') -> None:
            if hx.size() != expected_hidden_size:
                raise RuntimeError(msg.format(expected_hidden_size, list(hx.size())))

        def _weights_have_changed(self):
            # Returns True if the weight tensors have changed since the last forward pass.
            # This is the case when used with torch.func.functional_call(), for example.
            weights_changed = False
            for ref, name in zip(self._flat_weight_refs, self._flat_weights_names):
                weight = getattr(self, name) if hasattr(self, name) else None
                if weight is not None and ref is not None and ref() is not weight:
                    weights_changed = True
                    break
            return weights_changed

        def check_forward_args(self, input: Tensor, hidden: Tensor, batch_sizes: Optional[Tensor]):
            self.check_input(input, batch_sizes)
            expected_hidden_size = self.get_expected_hidden_size(input, batch_sizes)

            self.check_hidden_size(hidden, expected_hidden_size)

        def permute_hidden(self, hx: Tensor, permutation: Optional[Tensor]):
            if permutation is None:
                return hx
            return _apply_permutation(hx, permutation)

        def extra_repr(self) -> str:
            s = '{input_size}, {hidden_size}'
            if self.proj_size != 0:
                s += ', proj_size={proj_size}'
            if self.num_layers != 1:
                s += ', num_layers={num_layers}'
            if self.bias is not True:
                s += ', bias={bias}'
            if self.batch_first is not False:
                s += ', batch_first={batch_first}'
            if self.dropout != 0:
                s += ', dropout={dropout}'
            if self.bidirectional is not False:
                s += ', bidirectional={bidirectional}'
            return s.format(**self.__dict__)

        def _update_flat_weights(self):
            if not torch.jit.is_scripting():
                if self._weights_have_changed():
                    self._init_flat_weights()

        def __getstate__(self):
            # If weights have been changed, update the _flat_weights in __getstate__ here.
            self._update_flat_weights()
            # Don't serialize the weight references.
            state = self.__dict__.copy()
            del state['_flat_weight_refs']
            return state

        def __setstate__(self, d):
            super().__setstate__(d)
            if 'all_weights' in d:
                self._all_weights = d['all_weights']
            # In PyTorch 1.8 we added a proj_size member variable to LSTM.
            # LSTMs that were serialized via torch.save(module) before PyTorch 1.8
            # don't have it, so to preserve compatibility we set proj_size here.
            if 'proj_size' not in d:
                self.proj_size = 0

            if not isinstance(self._all_weights[0][0], str):
                num_layers = self.num_layers
                num_directions = 2 if self.bidirectional else 1
                self._flat_weights_names = []
                self._all_weights = []
                for layer in range(num_layers):
                    for direction in range(num_directions):
                        suffix = '_reverse' if direction == 1 else ''
                        weights = ['weight_ih_l{}{}', 'weight_hh_l{}{}', 'bias_ih_l{}{}',
                                   'bias_hh_l{}{}', 'weight_hr_l{}{}']
                        weights = [x.format(layer, suffix) for x in weights]
                        if self.bias:
                            if self.proj_size > 0:
                                self._all_weights += [weights]
                                self._flat_weights_names.extend(weights)
                            else:
                                self._all_weights += [weights[:4]]
                                self._flat_weights_names.extend(weights[:4])
                        else:
                            if self.proj_size > 0:
                                self._all_weights += [weights[:2]] + [weights[-1:]]
                                self._flat_weights_names.extend(weights[:2] + [weights[-1:]])
                            else:
                                self._all_weights += [weights[:2]]
                                self._flat_weights_names.extend(weights[:2])
                self._flat_weights = [getattr(self, wn) if hasattr(self, wn) else None
                                      for wn in self._flat_weights_names]

            self._flat_weight_refs = [weakref.ref(w) if w is not None else None
                                      for w in self._flat_weights]

        @property
        def all_weights(self) -> List[List[Parameter]]:
            return [[getattr(self, weight) for weight in weights] for weights in self._all_weights]

        def _replicate_for_data_parallel(self):
            replica = super()._replicate_for_data_parallel()
            # Need to copy these caches, otherwise the replica will share the same
            # flat weights list.
            replica._flat_weights = replica._flat_weights[:]
            replica._flat_weights_names = replica._flat_weights_names[:]
            return replica

    class RNN(RNNBase):
        r"""__init__(self,input_size,hidden_size,num_layers=1,nonlinearity='tanh',bias=True,batch_first=False,dropout=0.0,bidirectional=False,device=None,dtype=None)

        Applies a multi-layer Elman RNN with :math:`\tanh` or :math:`\text{ReLU}` non-linearity to an
        input sequence.

        For each element in the input sequence, each layer computes the following
        function:

        .. math::
            h_t = \tanh(x_t W_{ih}^T + b_{ih} + h_{t-1}W_{hh}^T + b_{hh})

        where :math:`h_t` is the hidden state at time `t`, :math:`x_t` is
        the input at time `t`, and :math:`h_{(t-1)}` is the hidden state of the
        previous layer at time `t-1` or the initial hidden state at time `0`.
        If :attr:`nonlinearity` is ``'relu'``, then :math:`\text{ReLU}` is used instead of :math:`\tanh`.

        Args:
            input_size: The number of expected features in the input `x`
            hidden_size: The number of features in the hidden state `h`
            num_layers: Number of recurrent layers. E.g., setting ``num_layers=2``
                would mean stacking two RNNs together to form a `stacked RNN`,
                with the second RNN taking in outputs of the first RNN and
                computing the final results. Default: 1
            nonlinearity: The non-linearity to use. Can be either ``'tanh'`` or ``'relu'``. Default: ``'tanh'``
            bias: If ``False``, then the layer does not use bias weights `b_ih` and `b_hh`.
                Default: ``True``
            batch_first: If ``True``, then the input and output tensors are provided
                as `(batch, seq, feature)` instead of `(seq, batch, feature)`.
                Note that this does not apply to hidden or cell states. See the
                Inputs/Outputs sections below for details.  Default: ``False``
            dropout: If non-zero, introduces a `Dropout` layer on the outputs of each
                RNN layer except the last layer, with dropout probability equal to
                :attr:`dropout`. Default: 0
            bidirectional: If ``True``, becomes a bidirectional RNN. Default: ``False``

        Inputs: input, h_0
            * **input**: tensor of shape :math:`(L, H_{in})` for unbatched input,
              :math:`(L, N, H_{in})` when ``batch_first=False`` or
              :math:`(N, L, H_{in})` when ``batch_first=True`` containing the features of
              the input sequence.  The input can also be a packed variable length sequence.
              See :func:`torch.nn.utils.rnn.pack_padded_sequence` or
              :func:`torch.nn.utils.rnn.pack_sequence` for details.
            * **h_0**: tensor of shape :math:`(D * \text{num\_layers}, H_{out})` for unbatched input or
              :math:`(D * \text{num\_layers}, N, H_{out})` containing the initial hidden
              state for the input sequence batch. Defaults to zeros if not provided.

            where:

            .. math::
                \begin{aligned}
                    N ={} & \text{batch size} \\
                    L ={} & \text{sequence length} \\
                    D ={} & 2 \text{ if bidirectional=True otherwise } 1 \\
                    H_{in} ={} & \text{input\_size} \\
                    H_{out} ={} & \text{hidden\_size}
                \end{aligned}

        Outputs: output, h_n
            * **output**: tensor of shape :math:`(L, D * H_{out})` for unbatched input,
              :math:`(L, N, D * H_{out})` when ``batch_first=False`` or
              :math:`(N, L, D * H_{out})` when ``batch_first=True`` containing the output features
              `(h_t)` from the last layer of the RNN, for each `t`. If a
              :class:`torch.nn.utils.rnn.PackedSequence` has been given as the input, the output
              will also be a packed sequence.
            * **h_n**: tensor of shape :math:`(D * \text{num\_layers}, H_{out})` for unbatched input or
              :math:`(D * \text{num\_layers}, N, H_{out})` containing the final hidden state
              for each element in the batch.

        Attributes:
            weight_ih_l[k]: the learnable input-hidden weights of the k-th layer,
                of shape `(hidden_size, input_size)` for `k = 0`. Otherwise, the shape is
                `(hidden_size, num_directions * hidden_size)`
            weight_hh_l[k]: the learnable hidden-hidden weights of the k-th layer,
                of shape `(hidden_size, hidden_size)`
            bias_ih_l[k]: the learnable input-hidden bias of the k-th layer,
                of shape `(hidden_size)`
            bias_hh_l[k]: the learnable hidden-hidden bias of the k-th layer,
                of shape `(hidden_size)`

        .. note::
            All the weights and biases are initialized from :math:`\mathcal{U}(-\sqrt{k}, \sqrt{k})`
            where :math:`k = \frac{1}{\text{hidden\_size}}`

        .. note::
            For bidirectional RNNs, forward and backward are directions 0 and 1 respectively.
            Example of splitting the output layers when ``batch_first=False``:
            ``output.view(seq_len, batch, num_directions, hidden_size)``.

        .. note::
            ``batch_first`` argument is ignored for unbatched inputs.

        .. include:: ../cudnn_rnn_determinism.rst

        .. include:: ../cudnn_persistent_rnn.rst

        Examples::

            >>> rnn = nn.RNN(10, 20, 2)
            >>> input = torch.randn(5, 3, 10)
            >>> h0 = torch.randn(2, 3, 20)
            >>> output, hn = rnn(input, h0)
        """

        @overload
        def __init__(self, input_size: int, hidden_size: int, num_layers: int = 1,
                     nonlinearity: str = 'tanh', bias: bool = True, batch_first: bool = False,
                     dropout: float = 0., bidirectional: bool = False, device=None,
                     dtype=None) -> None:
            ...

        @overload
        def __init__(self, *args, **kwargs):
            ...

        def __init__(self, *args, **kwargs):
            if 'proj_size' in kwargs:
                raise ValueError("proj_size argument is only supported for LSTM, not RNN or GRU")
            self.nonlinearity = kwargs.pop('nonlinearity', 'tanh')
            if self.nonlinearity == 'tanh':
                mode = 'RNN_TANH'
            elif self.nonlinearity == 'relu':
                mode = 'RNN_RELU'
            else:
                raise ValueError(f"Unknown nonlinearity '{self.nonlinearity}'. Select from 'tanh' or 'relu'.")
            super().__init__(mode, *args, **kwargs)

        @overload
        @torch._jit_internal._overload_method  # noqa: F811
        def forward(self, input: Tensor, hx: Optional[Tensor] = None) -> Tuple[Tensor, Tensor]:
            pass

        @overload
        @torch._jit_internal._overload_method  # noqa: F811
        def forward(self, input: PackedSequence, hx: Optional[Tensor] = None) -> Tuple[PackedSequence, Tensor]:
            pass

        def forward(self, input, hx=None):  # noqa: F811
            self._update_flat_weights()

            num_directions = 2 if self.bidirectional else 1
            orig_input = input

            if isinstance(orig_input, PackedSequence):
                input, batch_sizes, sorted_indices, unsorted_indices = input
                max_batch_size = batch_sizes[0]
                # script() is unhappy when max_batch_size is different type in cond branches, so we duplicate
                if hx is None:
                    hx = torch.zeros(self.num_layers * num_directions,
                                     max_batch_size, self.hidden_size,
                                     dtype=input.dtype, device=input.device)
                else:
                    # Each batch of the hidden state should match the input sequence that
                    # the user believes he/she is passing in.
                    hx = self.permute_hidden(hx, sorted_indices)
            else:
                batch_sizes = None
                if input.dim() not in (2, 3):
                    raise ValueError(f"RNN: Expected input to be 2D or 3D, got {input.dim()}D tensor instead")
                is_batched = input.dim() == 3
                batch_dim = 0 if self.batch_first else 1
                if not is_batched:
                    input = input.unsqueeze(batch_dim)
                    if hx is not None:
                        if hx.dim() != 2:
                            raise RuntimeError(
                                f"For unbatched 2-D input, hx should also be 2-D but got {hx.dim()}-D tensor")
                        hx = hx.unsqueeze(1)
                else:
                    if hx is not None and hx.dim() != 3:
                        raise RuntimeError(
                            f"For batched 3-D input, hx should also be 3-D but got {hx.dim()}-D tensor")
                max_batch_size = input.size(0) if self.batch_first else input.size(1)
                sorted_indices = None
                unsorted_indices = None
                if hx is None:
                    hx = torch.zeros(self.num_layers * num_directions,
                                     max_batch_size, self.hidden_size,
                                     dtype=input.dtype, device=input.device)
                else:
                    # Each batch of the hidden state should match the input sequence that
                    # the user believes he/she is passing in.
                    hx = self.permute_hidden(hx, sorted_indices)

            assert hx is not None
            self.check_forward_args(input, hx, batch_sizes)
            assert self.mode == 'RNN_TANH' or self.mode == 'RNN_RELU'
            if batch_sizes is None:
                if self.mode == 'RNN_TANH':
                    result = _VF.rnn_tanh(input, hx, self._flat_weights, self.bias, self.num_layers,
                                          self.dropout, self.training, self.bidirectional,
                                          self.batch_first)
                else:
                    result = _VF.rnn_relu(input, hx, self._flat_weights, self.bias, self.num_layers,
                                          self.dropout, self.training, self.bidirectional,
                                          self.batch_first)
            else:
                if self.mode == 'RNN_TANH':
                    result = _VF.rnn_tanh(input, batch_sizes, hx, self._flat_weights, self.bias,
                                          self.num_layers, self.dropout, self.training,
                                          self.bidirectional)
                else:
                    result = _VF.rnn_relu(input, batch_sizes, hx, self._flat_weights, self.bias,
                                          self.num_layers, self.dropout, self.training,
                                          self.bidirectional)

            output = result[0]
            hidden = result[1]

            if isinstance(orig_input, PackedSequence):
                output_packed = PackedSequence(output, batch_sizes, sorted_indices, unsorted_indices)
                return output_packed, self.permute_hidden(hidden, unsorted_indices)

            if not is_batched:
                output = output.squeeze(batch_dim)
                hidden = hidden.squeeze(1)

            return output, self.permute_hidden(hidden, unsorted_indices)

    # XXX: LSTM and GRU implementation is different from RNNBase, this is because:
    # 1. we want to support nn.LSTM and nn.GRU in TorchScript and TorchScript in
    #    its current state could not support the python Union Type or Any Type
    # 2. TorchScript static typing does not allow a Function or Callable type in
    #    Dict values, so we have to separately call _VF instead of using _rnn_impls
    # 3. This is temporary only and in the transition state that we want to make it
    #    on time for the release
    #
    # More discussion details in https://github.com/pytorch/pytorch/pull/23266
    #
    # TODO: remove the overriding implementations for LSTM and GRU when TorchScript
    # support expressing these two modules generally.

    class LSTM(RNNBase):
        r"""__init__(self,input_size,hidden_size,num_layers=1,bias=True,batch_first=False,dropout=0.0,bidirectional=False,proj_size=0,device=None,dtype=None)

        Applies a multi-layer long short-term memory (LSTM) RNN to an input
        sequence.

        For each element in the input sequence, each layer computes the following
        function:

        .. math::
            \begin{array}{ll} \\
                i_t = \sigma(W_{ii} x_t + b_{ii} + W_{hi} h_{t-1} + b_{hi}) \\
                f_t = \sigma(W_{if} x_t + b_{if} + W_{hf} h_{t-1} + b_{hf}) \\
                g_t = \tanh(W_{ig} x_t + b_{ig} + W_{hg} h_{t-1} + b_{hg}) \\
                o_t = \sigma(W_{io} x_t + b_{io} + W_{ho} h_{t-1} + b_{ho}) \\
                c_t = f_t \odot c_{t-1} + i_t \odot g_t \\
                h_t = o_t \odot \tanh(c_t) \\
            \end{array}

        where :math:`h_t` is the hidden state at time `t`, :math:`c_t` is the cell
        state at time `t`, :math:`x_t` is the input at time `t`, :math:`h_{t-1}`
        is the hidden state of the layer at time `t-1` or the initial hidden
        state at time `0`, and :math:`i_t`, :math:`f_t`, :math:`g_t`,
        :math:`o_t` are the input, forget, cell, and output gates, respectively.
        :math:`\sigma` is the sigmoid function, and :math:`\odot` is the Hadamard product.

        In a multilayer LSTM, the input :math:`x^{(l)}_t` of the :math:`l` -th layer
        (:math:`l \ge 2`) is the hidden state :math:`h^{(l-1)}_t` of the previous layer multiplied by
        dropout :math:`\delta^{(l-1)}_t` where each :math:`\delta^{(l-1)}_t` is a Bernoulli random
        variable which is :math:`0` with probability :attr:`dropout`.

        If ``proj_size > 0`` is specified, LSTM with projections will be used. This changes
        the LSTM cell in the following way. First, the dimension of :math:`h_t` will be changed from
        ``hidden_size`` to ``proj_size`` (dimensions of :math:`W_{hi}` will be changed accordingly).
        Second, the output hidden state of each layer will be multiplied by a learnable projection
        matrix: :math:`h_t = W_{hr}h_t`. Note that as a consequence of this, the output
        of LSTM network will be of different shape as well. See Inputs/Outputs sections below for exact
        dimensions of all variables. You can find more details in https://arxiv.org/abs/1402.1128.

        Args:
            input_size: The number of expected features in the input `x`
            hidden_size: The number of features in the hidden state `h`
            num_layers: Number of recurrent layers. E.g., setting ``num_layers=2``
                would mean stacking two LSTMs together to form a `stacked LSTM`,
                with the second LSTM taking in outputs of the first LSTM and
                computing the final results. Default: 1
            bias: If ``False``, then the layer does not use bias weights `b_ih` and `b_hh`.
                Default: ``True``
            batch_first: If ``True``, then the input and output tensors are provided
                as `(batch, seq, feature)` instead of `(seq, batch, feature)`.
                Note that this does not apply to hidden or cell states. See the
                Inputs/Outputs sections below for details.  Default: ``False``
            dropout: If non-zero, introduces a `Dropout` layer on the outputs of each
                LSTM layer except the last layer, with dropout probability equal to
                :attr:`dropout`. Default: 0
            bidirectional: If ``True``, becomes a bidirectional LSTM. Default: ``False``
            proj_size: If ``> 0``, will use LSTM with projections of corresponding size. Default: 0

        Inputs: input, (h_0, c_0)
            * **input**: tensor of shape :math:`(L, H_{in})` for unbatched input,
              :math:`(L, N, H_{in})` when ``batch_first=False`` or
              :math:`(N, L, H_{in})` when ``batch_first=True`` containing the features of
              the input sequence.  The input can also be a packed variable length sequence.
              See :func:`torch.nn.utils.rnn.pack_padded_sequence` or
              :func:`torch.nn.utils.rnn.pack_sequence` for details.
            * **h_0**: tensor of shape :math:`(D * \text{num\_layers}, H_{out})` for unbatched input or
              :math:`(D * \text{num\_layers}, N, H_{out})` containing the
              initial hidden state for each element in the input sequence.
              Defaults to zeros if (h_0, c_0) is not provided.
            * **c_0**: tensor of shape :math:`(D * \text{num\_layers}, H_{cell})` for unbatched input or
              :math:`(D * \text{num\_layers}, N, H_{cell})` containing the
              initial cell state for each element in the input sequence.
              Defaults to zeros if (h_0, c_0) is not provided.

            where:

            .. math::
                \begin{aligned}
                    N ={} & \text{batch size} \\
                    L ={} & \text{sequence length} \\
                    D ={} & 2 \text{ if bidirectional=True otherwise } 1 \\
                    H_{in} ={} & \text{input\_size} \\
                    H_{cell} ={} & \text{hidden\_size} \\
                    H_{out} ={} & \text{proj\_size if } \text{proj\_size}>0 \text{ otherwise hidden\_size} \\
                \end{aligned}

        Outputs: output, (h_n, c_n)
            * **output**: tensor of shape :math:`(L, D * H_{out})` for unbatched input,
              :math:`(L, N, D * H_{out})` when ``batch_first=False`` or
              :math:`(N, L, D * H_{out})` when ``batch_first=True`` containing the output features
              `(h_t)` from the last layer of the LSTM, for each `t`. If a
              :class:`torch.nn.utils.rnn.PackedSequence` has been given as the input, the output
              will also be a packed sequence. When ``bidirectional=True``, `output` will contain
              a concatenation of the forward and reverse hidden states at each time step in the sequence.
            * **h_n**: tensor of shape :math:`(D * \text{num\_layers}, H_{out})` for unbatched input or
              :math:`(D * \text{num\_layers}, N, H_{out})` containing the
              final hidden state for each element in the sequence. When ``bidirectional=True``,
              `h_n` will contain a concatenation of the final forward and reverse hidden states, respectively.
            * **c_n**: tensor of shape :math:`(D * \text{num\_layers}, H_{cell})` for unbatched input or
              :math:`(D * \text{num\_layers}, N, H_{cell})` containing the
              final cell state for each element in the sequence. When ``bidirectional=True``,
              `c_n` will contain a concatenation of the final forward and reverse cell states, respectively.

        Attributes:
            weight_ih_l[k] : the learnable input-hidden weights of the :math:`\text{k}^{th}` layer
                `(W_ii|W_if|W_ig|W_io)`, of shape `(4*hidden_size, input_size)` for `k = 0`.
                Otherwise, the shape is `(4*hidden_size, num_directions * hidden_size)`. If
                ``proj_size > 0`` was specified, the shape will be
                `(4*hidden_size, num_directions * proj_size)` for `k > 0`
            weight_hh_l[k] : the learnable hidden-hidden weights of the :math:`\text{k}^{th}` layer
                `(W_hi|W_hf|W_hg|W_ho)`, of shape `(4*hidden_size, hidden_size)`. If ``proj_size > 0``
                was specified, the shape will be `(4*hidden_size, proj_size)`.
            bias_ih_l[k] : the learnable input-hidden bias of the :math:`\text{k}^{th}` layer
                `(b_ii|b_if|b_ig|b_io)`, of shape `(4*hidden_size)`
            bias_hh_l[k] : the learnable hidden-hidden bias of the :math:`\text{k}^{th}` layer
                `(b_hi|b_hf|b_hg|b_ho)`, of shape `(4*hidden_size)`
            weight_hr_l[k] : the learnable projection weights of the :math:`\text{k}^{th}` layer
                of shape `(proj_size, hidden_size)`. Only present when ``proj_size > 0`` was
                specified.
            weight_ih_l[k]_reverse: Analogous to `weight_ih_l[k]` for the reverse direction.
                Only present when ``bidirectional=True``.
            weight_hh_l[k]_reverse:  Analogous to `weight_hh_l[k]` for the reverse direction.
                Only present when ``bidirectional=True``.
            bias_ih_l[k]_reverse:  Analogous to `bias_ih_l[k]` for the reverse direction.
                Only present when ``bidirectional=True``.
            bias_hh_l[k]_reverse:  Analogous to `bias_hh_l[k]` for the reverse direction.
                Only present when ``bidirectional=True``.
            weight_hr_l[k]_reverse:  Analogous to `weight_hr_l[k]` for the reverse direction.
                Only present when ``bidirectional=True`` and ``proj_size > 0`` was specified.

        .. note::
            All the weights and biases are initialized from :math:`\mathcal{U}(-\sqrt{k}, \sqrt{k})`
            where :math:`k = \frac{1}{\text{hidden\_size}}`

        .. note::
            For bidirectional LSTMs, forward and backward are directions 0 and 1 respectively.
            Example of splitting the output layers when ``batch_first=False``:
            ``output.view(seq_len, batch, num_directions, hidden_size)``.

        .. note::
            For bidirectional LSTMs, `h_n` is not equivalent to the last element of `output`; the
            former contains the final forward and reverse hidden states, while the latter contains the
            final forward hidden state and the initial reverse hidden state.

        .. note::
            ``batch_first`` argument is ignored for unbatched inputs.

        .. note::
            ``proj_size`` should be smaller than ``hidden_size``.

        .. include:: ../cudnn_rnn_determinism.rst

        .. include:: ../cudnn_persistent_rnn.rst

        Examples::

            >>> rnn = nn.LSTM(10, 20, 2)
            >>> input = torch.randn(5, 3, 10)
            >>> h0 = torch.randn(2, 3, 20)
            >>> c0 = torch.randn(2, 3, 20)
            >>> output, (hn, cn) = rnn(input, (h0, c0))
        """

        @overload
        def __init__(self, input_size: int, hidden_size: int, num_layers: int = 1, bias: bool = True,
                     batch_first: bool = False, dropout: float = 0., bidirectional: bool = False,
                     proj_size: int = 0, device=None, dtype=None) -> None:
            ...

        @overload
        def __init__(self, *args, **kwargs):
            ...

        def __init__(self, *args, **kwargs):
            super().__init__('LSTM', *args, **kwargs)

        def get_expected_cell_size(self, input: Tensor, batch_sizes: Optional[Tensor]) -> Tuple[int, int, int]:
            if batch_sizes is not None:
                mini_batch = int(batch_sizes[0])
            else:
                mini_batch = input.size(0) if self.batch_first else input.size(1)
            num_directions = 2 if self.bidirectional else 1
            expected_hidden_size = (self.num_layers * num_directions,
                                    mini_batch, self.hidden_size)
            return expected_hidden_size

        # In the future, we should prevent mypy from applying contravariance rules here.
        # See torch/nn/modules/module.py::_forward_unimplemented
        def check_forward_args(self,  # type: ignore[override]
                               input: Tensor,
                               hidden: Tuple[Tensor, Tensor],
                               batch_sizes: Optional[Tensor],
                               ):
            self.check_input(input, batch_sizes)
            self.check_hidden_size(hidden[0], self.get_expected_hidden_size(input, batch_sizes),
                                   'Expected hidden[0] size {}, got {}')
            self.check_hidden_size(hidden[1], self.get_expected_cell_size(input, batch_sizes),
                                   'Expected hidden[1] size {}, got {}')

        # Same as above, see torch/nn/modules/module.py::_forward_unimplemented
        def permute_hidden(self,  # type: ignore[override]
                           hx: Tuple[Tensor, Tensor],
                           permutation: Optional[Tensor]
                           ) -> Tuple[Tensor, Tensor]:
            if permutation is None:
                return hx
            return _apply_permutation(hx[0], permutation), _apply_permutation(hx[1], permutation)

        # Same as above, see torch/nn/modules/module.py::_forward_unimplemented
        @overload  # type: ignore[override]
        @torch._jit_internal._overload_method  # noqa: F811
        def forward(self, input: Tensor, hx: Optional[Tuple[Tensor, Tensor]] = None
                    ) -> Tuple[Tensor, Tuple[Tensor, Tensor]]:  # noqa: F811
            pass

        # Same as above, see torch/nn/modules/module.py::_forward_unimplemented
        @overload
        @torch._jit_internal._overload_method  # noqa: F811
        def forward(self, input: PackedSequence, hx: Optional[Tuple[Tensor, Tensor]] = None
                    ) -> Tuple[PackedSequence, Tuple[Tensor, Tensor]]:  # noqa: F811
            pass

        def forward(self, input, hx=None):  # noqa: F811
            self._update_flat_weights()

            orig_input = input
            # xxx: isinstance check needs to be in conditional for TorchScript to compile
            batch_sizes = None
            do_permute = False
            num_directions = 2 if self.bidirectional else 1
            real_hidden_size = self.proj_size if self.proj_size > 0 else self.hidden_size
            if isinstance(orig_input, PackedSequence):
                input, batch_sizes, sorted_indices, unsorted_indices = input
                max_batch_size = batch_sizes[0]
                if hx is None:
                    h_zeros = torch.zeros(self.num_layers * num_directions,
                                          max_batch_size, real_hidden_size,
                                          dtype=input.dtype, device=input.device)
                    c_zeros = torch.zeros(self.num_layers * num_directions,
                                          max_batch_size, self.hidden_size,
                                          dtype=input.dtype, device=input.device)
                    hx = (h_zeros, c_zeros)
                else:
                    # Each batch of the hidden state should match the input sequence that
                    # the user believes he/she is passing in.
                    hx = self.permute_hidden(hx, sorted_indices)
            else:
                if input.dim() not in (2, 3):
                    raise ValueError(f"LSTM: Expected input to be 2D or 3D, got {input.dim()}D instead")
                is_batched = input.dim() == 3
                batch_dim = 0 if self.batch_first else 1
                if not is_batched:
                    input = input.unsqueeze(batch_dim)
                max_batch_size = input.size(0) if self.batch_first else input.size(1)
                sorted_indices = None
                unsorted_indices = None
                if hx is None:
                    h_zeros = torch.zeros(self.num_layers * num_directions,
                                          max_batch_size, real_hidden_size,
                                          dtype=input.dtype, device=input.device)
                    c_zeros = torch.zeros(self.num_layers * num_directions,
                                          max_batch_size, self.hidden_size,
                                          dtype=input.dtype, device=input.device)
                    hx = (h_zeros, c_zeros)
                else:
                    if is_batched:
                        if (hx[0].dim() != 3 or hx[1].dim() != 3):
                            msg = ("For batched 3-D input, hx and cx should "
                                   f"also be 3-D but got ({hx[0].dim()}-D, {hx[1].dim()}-D) tensors")
                            raise RuntimeError(msg)
                    else:
                        if hx[0].dim() != 2 or hx[1].dim() != 2:
                            msg = ("For unbatched 2-D input, hx and cx should "
                                   f"also be 2-D but got ({hx[0].dim()}-D, {hx[1].dim()}-D) tensors")
                            raise RuntimeError(msg)
                        hx = (hx[0].unsqueeze(1), hx[1].unsqueeze(1))
                    # Each batch of the hidden state should match the input sequence that
                    # the user believes he/she is passing in.
                    self.check_forward_args(input, hx, batch_sizes)
                    hx = self.permute_hidden(hx, sorted_indices)

            if batch_sizes is None:
                result = _VF.lstm(input, hx, self._flat_weights, self.bias, self.num_layers,
                                  self.dropout, self.training, self.bidirectional, self.batch_first)
            else:
                result = _VF.lstm(input, batch_sizes, hx, self._flat_weights, self.bias,
                                  self.num_layers, self.dropout, self.training, self.bidirectional)
            output = result[0]
            hidden = result[1:]
            # xxx: isinstance check needs to be in conditional for TorchScript to compile
            if isinstance(orig_input, PackedSequence):
                output_packed = PackedSequence(output, batch_sizes, sorted_indices, unsorted_indices)
                return output_packed, self.permute_hidden(hidden, unsorted_indices)
            else:
                if not is_batched:
                    output = output.squeeze(batch_dim)
                    hidden = (hidden[0].squeeze(1), hidden[1].squeeze(1))
                return output, self.permute_hidden(hidden, unsorted_indices)

    class GRU(RNNBase):
        r"""__init__(self,input_size,hidden_size,num_layers=1,bias=True,batch_first=False,dropout=0.0,bidirectional=False,device=None,dtype=None)

        Applies a multi-layer gated recurrent unit (GRU) RNN to an input sequence.

        For each element in the input sequence, each layer computes the following
        function:

        .. math::
            \begin{array}{ll}
                r_t = \sigma(W_{ir} x_t + b_{ir} + W_{hr} h_{(t-1)} + b_{hr}) \\
                z_t = \sigma(W_{iz} x_t + b_{iz} + W_{hz} h_{(t-1)} + b_{hz}) \\
                n_t = \tanh(W_{in} x_t + b_{in} + r_t * (W_{hn} h_{(t-1)}+ b_{hn})) \\
                h_t = (1 - z_t) * n_t + z_t * h_{(t-1)}
            \end{array}

        where :math:`h_t` is the hidden state at time `t`, :math:`x_t` is the input
        at time `t`, :math:`h_{(t-1)}` is the hidden state of the layer
        at time `t-1` or the initial hidden state at time `0`, and :math:`r_t`,
        :math:`z_t`, :math:`n_t` are the reset, update, and new gates, respectively.
        :math:`\sigma` is the sigmoid function, and :math:`*` is the Hadamard product.

        In a multilayer GRU, the input :math:`x^{(l)}_t` of the :math:`l` -th layer
        (:math:`l \ge 2`) is the hidden state :math:`h^{(l-1)}_t` of the previous layer multiplied by
        dropout :math:`\delta^{(l-1)}_t` where each :math:`\delta^{(l-1)}_t` is a Bernoulli random
        variable which is :math:`0` with probability :attr:`dropout`.

        Args:
            input_size: The number of expected features in the input `x`
            hidden_size: The number of features in the hidden state `h`
            num_layers: Number of recurrent layers. E.g., setting ``num_layers=2``
                would mean stacking two GRUs together to form a `stacked GRU`,
                with the second GRU taking in outputs of the first GRU and
                computing the final results. Default: 1
            bias: If ``False``, then the layer does not use bias weights `b_ih` and `b_hh`.
                Default: ``True``
            batch_first: If ``True``, then the input and output tensors are provided
                as `(batch, seq, feature)` instead of `(seq, batch, feature)`.
                Note that this does not apply to hidden or cell states. See the
                Inputs/Outputs sections below for details.  Default: ``False``
            dropout: If non-zero, introduces a `Dropout` layer on the outputs of each
                GRU layer except the last layer, with dropout probability equal to
                :attr:`dropout`. Default: 0
            bidirectional: If ``True``, becomes a bidirectional GRU. Default: ``False``

        Inputs: input, h_0
            * **input**: tensor of shape :math:`(L, H_{in})` for unbatched input,
              :math:`(L, N, H_{in})` when ``batch_first=False`` or
              :math:`(N, L, H_{in})` when ``batch_first=True`` containing the features of
              the input sequence.  The input can also be a packed variable length sequence.
              See :func:`torch.nn.utils.rnn.pack_padded_sequence` or
              :func:`torch.nn.utils.rnn.pack_sequence` for details.
            * **h_0**: tensor of shape :math:`(D * \text{num\_layers}, H_{out})` or
              :math:`(D * \text{num\_layers}, N, H_{out})`
              containing the initial hidden state for the input sequence. Defaults to zeros if not provided.

            where:

            .. math::
                \begin{aligned}
                    N ={} & \text{batch size} \\
                    L ={} & \text{sequence length} \\
                    D ={} & 2 \text{ if bidirectional=True otherwise } 1 \\
                    H_{in} ={} & \text{input\_size} \\
                    H_{out} ={} & \text{hidden\_size}
                \end{aligned}

        Outputs: output, h_n
            * **output**: tensor of shape :math:`(L, D * H_{out})` for unbatched input,
              :math:`(L, N, D * H_{out})` when ``batch_first=False`` or
              :math:`(N, L, D * H_{out})` when ``batch_first=True`` containing the output features
              `(h_t)` from the last layer of the GRU, for each `t`. If a
              :class:`torch.nn.utils.rnn.PackedSequence` has been given as the input, the output
              will also be a packed sequence.
            * **h_n**: tensor of shape :math:`(D * \text{num\_layers}, H_{out})` or
              :math:`(D * \text{num\_layers}, N, H_{out})` containing the final hidden state
              for the input sequence.

        Attributes:
            weight_ih_l[k] : the learnable input-hidden weights of the :math:`\text{k}^{th}` layer
                (W_ir|W_iz|W_in), of shape `(3*hidden_size, input_size)` for `k = 0`.
                Otherwise, the shape is `(3*hidden_size, num_directions * hidden_size)`
            weight_hh_l[k] : the learnable hidden-hidden weights of the :math:`\text{k}^{th}` layer
                (W_hr|W_hz|W_hn), of shape `(3*hidden_size, hidden_size)`
            bias_ih_l[k] : the learnable input-hidden bias of the :math:`\text{k}^{th}` layer
                (b_ir|b_iz|b_in), of shape `(3*hidden_size)`
            bias_hh_l[k] : the learnable hidden-hidden bias of the :math:`\text{k}^{th}` layer
                (b_hr|b_hz|b_hn), of shape `(3*hidden_size)`

        .. note::
            All the weights and biases are initialized from :math:`\mathcal{U}(-\sqrt{k}, \sqrt{k})`
            where :math:`k = \frac{1}{\text{hidden\_size}}`

        .. note::
            For bidirectional GRUs, forward and backward are directions 0 and 1 respectively.
            Example of splitting the output layers when ``batch_first=False``:
            ``output.view(seq_len, batch, num_directions, hidden_size)``.

        .. note::
            ``batch_first`` argument is ignored for unbatched inputs.

        .. note::
            The calculation of new gate :math:`n_t` subtly differs from the original paper and other frameworks.
            In the original implementation, the Hadamard product :math:`(*)` between :math:`r_t` and the
            previous hidden state :math:`h_{(t-1)}` is done before the multiplication with the weight matrix
            `W` and addition of bias:

            .. math::
                \begin{aligned}
                    n_t = \tanh(W_{in} x_t + b_{in} + W_{hn} ( r_t * h_{(t-1)} ) + b_{hn})
                \end{aligned}

            This is in contrast to PyTorch implementation, which is done after :math:`W_{hn} h_{(t-1)}`

            .. math::
                \begin{aligned}
                    n_t = \tanh(W_{in} x_t + b_{in} + r_t * (W_{hn} h_{(t-1)}+ b_{hn}))
                \end{aligned}

            This implementation differs on purpose for efficiency.

        .. include:: ../cudnn_persistent_rnn.rst

        Examples::

            >>> rnn = nn.GRU(10, 20, 2)
            >>> input = torch.randn(5, 3, 10)
            >>> h0 = torch.randn(2, 3, 20)
            >>> output, hn = rnn(input, h0)
        """

        @overload
        def __init__(self, input_size: int, hidden_size: int, num_layers: int = 1, bias: bool = True,
                     batch_first: bool = False, dropout: float = 0., bidirectional: bool = False,
                     device=None, dtype=None) -> None:
            ...

        @overload
        def __init__(self, *args, **kwargs):
            ...

        def __init__(self, *args, **kwargs):
            if 'proj_size' in kwargs:
                raise ValueError("proj_size argument is only supported for LSTM, not RNN or GRU")
            super().__init__('GRU', *args, **kwargs)

        @overload  # type: ignore[override]
        @torch._jit_internal._overload_method  # noqa: F811
        def forward(self, input: Tensor, hx: Optional[Tensor] = None) -> Tuple[Tensor, Tensor]:  # noqa: F811
            pass

        @overload
        @torch._jit_internal._overload_method  # noqa: F811
        def forward(self, input: PackedSequence, hx: Optional[Tensor] = None) -> Tuple[PackedSequence, Tensor]:  # noqa: F811
            pass

        def forward(self, input, hx=None):  # noqa: F811
            self._update_flat_weights()

            orig_input = input
            # xxx: isinstance check needs to be in conditional for TorchScript to compile
            if isinstance(orig_input, PackedSequence):
                input, batch_sizes, sorted_indices, unsorted_indices = input
                max_batch_size = batch_sizes[0]
                if hx is None:
                    num_directions = 2 if self.bidirectional else 1
                    hx = torch.zeros(self.num_layers * num_directions,
                                     max_batch_size, self.hidden_size,
                                     dtype=input.dtype, device=input.device)
                else:
                    # Each batch of the hidden state should match the input sequence that
                    # the user believes he/she is passing in.
                    hx = self.permute_hidden(hx, sorted_indices)
            else:
                batch_sizes = None
                if input.dim() not in (2, 3):
                    raise ValueError(f"GRU: Expected input to be 2D or 3D, got {input.dim()}D instead")
                is_batched = input.dim() == 3
                batch_dim = 0 if self.batch_first else 1
                if not is_batched:
                    input = input.unsqueeze(batch_dim)
                    if hx is not None:
                        if hx.dim() != 2:
                            raise RuntimeError(
                                f"For unbatched 2-D input, hx should also be 2-D but got {hx.dim()}-D tensor")
                        hx = hx.unsqueeze(1)
                else:
                    if hx is not None and hx.dim() != 3:
                        raise RuntimeError(
                            f"For batched 3-D input, hx should also be 3-D but got {hx.dim()}-D tensor")
                max_batch_size = input.size(0) if self.batch_first else input.size(1)
                sorted_indices = None
                unsorted_indices = None
                if hx is None:
                    num_directions = 2 if self.bidirectional else 1
                    hx = torch.zeros(self.num_layers * num_directions,
                                     max_batch_size, self.hidden_size,
                                     dtype=input.dtype, device=input.device)
                else:
                    # Each batch of the hidden state should match the input sequence that
                    # the user believes he/she is passing in.
                    hx = self.permute_hidden(hx, sorted_indices)

            self.check_forward_args(input, hx, batch_sizes)
            if batch_sizes is None:
                result = _VF.gru(input, hx, self._flat_weights, self.bias, self.num_layers,
                                 self.dropout, self.training, self.bidirectional, self.batch_first)
            else:
                result = _VF.gru(input, batch_sizes, hx, self._flat_weights, self.bias,
                                 self.num_layers, self.dropout, self.training, self.bidirectional)
            output = result[0]
            hidden = result[1]

            # xxx: isinstance check needs to be in conditional for TorchScript to compile
            if isinstance(orig_input, PackedSequence):
                output_packed = PackedSequence(output, batch_sizes, sorted_indices, unsorted_indices)
                return output_packed, self.permute_hidden(hidden, unsorted_indices)
            else:
                if not is_batched:
                    output = output.squeeze(batch_dim)
                    hidden = hidden.squeeze(1)

                return output, self.permute_hidden(hidden, unsorted_indices)

    class RNNCellBase(Module):
        __constants__ = ['input_size', 'hidden_size', 'bias']

        input_size: int
        hidden_size: int
        bias: bool
        weight_ih: Tensor
        weight_hh: Tensor
        # WARNING: bias_ih and bias_hh purposely not defined here.
        # See https://github.com/pytorch/pytorch/issues/39670

        def __init__(self, input_size: int, hidden_size: int, bias: bool, num_chunks: int,
                     device=None, dtype=None) -> None:
            factory_kwargs = {'device': device, 'dtype': dtype}
            super().__init__()
            self.input_size = input_size
            self.hidden_size = hidden_size
            self.bias = bias
            self.weight_ih = Parameter(torch.empty((num_chunks * hidden_size, input_size), **factory_kwargs))
            self.weight_hh = Parameter(torch.empty((num_chunks * hidden_size, hidden_size), **factory_kwargs))
            if bias:
                self.bias_ih = Parameter(torch.empty(num_chunks * hidden_size, **factory_kwargs))
                self.bias_hh = Parameter(torch.empty(num_chunks * hidden_size, **factory_kwargs))
            else:
                self.register_parameter('bias_ih', None)
                self.register_parameter('bias_hh', None)

            self.reset_parameters()

        def extra_repr(self) -> str:
            s = '{input_size}, {hidden_size}'
            if 'bias' in self.__dict__ and self.bias is not True:
                s += ', bias={bias}'
            if 'nonlinearity' in self.__dict__ and self.nonlinearity != "tanh":
                s += ', nonlinearity={nonlinearity}'
            return s.format(**self.__dict__)

        def reset_parameters(self) -> None:
            stdv = 1.0 / math.sqrt(self.hidden_size) if self.hidden_size > 0 else 0
            for weight in self.parameters():
                init.uniform_(weight, -stdv, stdv)

    class RNNCell(RNNCellBase):
        r"""An Elman RNN cell with tanh or ReLU non-linearity.

        .. math::

            h' = \tanh(W_{ih} x + b_{ih}  +  W_{hh} h + b_{hh})

        If :attr:`nonlinearity` is `'relu'`, then ReLU is used in place of tanh.

        Args:
            input_size: The number of expected features in the input `x`
            hidden_size: The number of features in the hidden state `h`
            bias: If ``False``, then the layer does not use bias weights `b_ih` and `b_hh`.
                Default: ``True``
            nonlinearity: The non-linearity to use. Can be either ``'tanh'`` or ``'relu'``. Default: ``'tanh'``

        Inputs: input, hidden
            - **input**: tensor containing input features
            - **hidden**: tensor containing the initial hidden state
              Defaults to zero if not provided.

        Outputs: h'
            - **h'** of shape `(batch, hidden_size)`: tensor containing the next hidden state
              for each element in the batch

        Shape:
            - input: :math:`(N, H_{in})` or :math:`(H_{in})` tensor containing input features where
              :math:`H_{in}` = `input_size`.
            - hidden: :math:`(N, H_{out})` or :math:`(H_{out})` tensor containing the initial hidden
              state where :math:`H_{out}` = `hidden_size`. Defaults to zero if not provided.
            - output: :math:`(N, H_{out})` or :math:`(H_{out})` tensor containing the next hidden state.

        Attributes:
            weight_ih: the learnable input-hidden weights, of shape
                `(hidden_size, input_size)`
            weight_hh: the learnable hidden-hidden weights, of shape
                `(hidden_size, hidden_size)`
            bias_ih: the learnable input-hidden bias, of shape `(hidden_size)`
            bias_hh: the learnable hidden-hidden bias, of shape `(hidden_size)`

        .. note::
            All the weights and biases are initialized from :math:`\mathcal{U}(-\sqrt{k}, \sqrt{k})`
            where :math:`k = \frac{1}{\text{hidden\_size}}`

        Examples::

            >>> rnn = nn.RNNCell(10, 20)
            >>> input = torch.randn(6, 3, 10)
            >>> hx = torch.randn(3, 20)
            >>> output = []
            >>> for i in range(6):
            ...     hx = rnn(input[i], hx)
            ...     output.append(hx)
        """
        __constants__ = ['input_size', 'hidden_size', 'bias', 'nonlinearity']
        nonlinearity: str

        def __init__(self, input_size: int, hidden_size: int, bias: bool = True, nonlinearity: str = "tanh",
                     device=None, dtype=None) -> None:
            factory_kwargs = {'device': device, 'dtype': dtype}
            super().__init__(input_size, hidden_size, bias, num_chunks=1, **factory_kwargs)
            self.nonlinearity = nonlinearity

        def forward(self, input: Tensor, hx: Optional[Tensor] = None) -> Tensor:
            if input.dim() not in (1, 2):
                raise ValueError(f"RNNCell: Expected input to be 1D or 2D, got {input.dim()}D instead")
            if hx is not None and hx.dim() not in (1, 2):
                raise ValueError(f"RNNCell: Expected hidden to be 1D or 2D, got {hx.dim()}D instead")
            is_batched = input.dim() == 2
            if not is_batched:
                input = input.unsqueeze(0)

            if hx is None:
                hx = torch.zeros(input.size(0), self.hidden_size, dtype=input.dtype, device=input.device)
            else:
                hx = hx.unsqueeze(0) if not is_batched else hx

            if self.nonlinearity == "tanh":
                ret = _VF.rnn_tanh_cell(
                    input, hx,
                    self.weight_ih, self.weight_hh,
                    self.bias_ih, self.bias_hh,
                )
            elif self.nonlinearity == "relu":
                ret = _VF.rnn_relu_cell(
                    input, hx,
                    self.weight_ih, self.weight_hh,
                    self.bias_ih, self.bias_hh,
                )
            else:
                ret = input  # TODO: remove when jit supports exception flow
                raise RuntimeError(
                    f"Unknown nonlinearity: {self.nonlinearity}")

            if not is_batched:
                ret = ret.squeeze(0)

            return ret

    class LSTMCell(RNNCellBase):
        r"""A long short-term memory (LSTM) cell.

        .. math::

            \begin{array}{ll}
            i = \sigma(W_{ii} x + b_{ii} + W_{hi} h + b_{hi}) \\
            f = \sigma(W_{if} x + b_{if} + W_{hf} h + b_{hf}) \\
            g = \tanh(W_{ig} x + b_{ig} + W_{hg} h + b_{hg}) \\
            o = \sigma(W_{io} x + b_{io} + W_{ho} h + b_{ho}) \\
            c' = f * c + i * g \\
            h' = o * \tanh(c') \\
            \end{array}

        where :math:`\sigma` is the sigmoid function, and :math:`*` is the Hadamard product.

        Args:
            input_size: The number of expected features in the input `x`
            hidden_size: The number of features in the hidden state `h`
            bias: If ``False``, then the layer does not use bias weights `b_ih` and
                `b_hh`. Default: ``True``

        Inputs: input, (h_0, c_0)
            - **input** of shape `(batch, input_size)` or `(input_size)`: tensor containing input features
            - **h_0** of shape `(batch, hidden_size)` or `(hidden_size)`: tensor containing the initial hidden state
            - **c_0** of shape `(batch, hidden_size)` or `(hidden_size)`: tensor containing the initial cell state

              If `(h_0, c_0)` is not provided, both **h_0** and **c_0** default to zero.

        Outputs: (h_1, c_1)
            - **h_1** of shape `(batch, hidden_size)` or `(hidden_size)`: tensor containing the next hidden state
            - **c_1** of shape `(batch, hidden_size)` or `(hidden_size)`: tensor containing the next cell state

        Attributes:
            weight_ih: the learnable input-hidden weights, of shape
                `(4*hidden_size, input_size)`
            weight_hh: the learnable hidden-hidden weights, of shape
                `(4*hidden_size, hidden_size)`
            bias_ih: the learnable input-hidden bias, of shape `(4*hidden_size)`
            bias_hh: the learnable hidden-hidden bias, of shape `(4*hidden_size)`

        .. note::
            All the weights and biases are initialized from :math:`\mathcal{U}(-\sqrt{k}, \sqrt{k})`
            where :math:`k = \frac{1}{\text{hidden\_size}}`

        On certain ROCm devices, when using float16 inputs this module will use :ref:`different precision<fp16_on_mi200>` for backward.

        Examples::

            >>> rnn = nn.LSTMCell(10, 20)  # (input_size, hidden_size)
            >>> input = torch.randn(2, 3, 10)  # (time_steps, batch, input_size)
            >>> hx = torch.randn(3, 20)  # (batch, hidden_size)
            >>> cx = torch.randn(3, 20)
            >>> output = []
            >>> for i in range(input.size()[0]):
            ...     hx, cx = rnn(input[i], (hx, cx))
            ...     output.append(hx)
            >>> output = torch.stack(output, dim=0)
        """

        def __init__(self, input_size: int, hidden_size: int, bias: bool = True,
                     device=None, dtype=None) -> None:
            factory_kwargs = {'device': device, 'dtype': dtype}
            super().__init__(input_size, hidden_size, bias, num_chunks=4, **factory_kwargs)

        def forward(self, input: Tensor, hx: Optional[Tuple[Tensor, Tensor]] = None) -> Tuple[Tensor, Tensor]:
            if input.dim() not in (1, 2):
                raise ValueError(f"LSTMCell: Expected input to be 1D or 2D, got {input.dim()}D instead")
            if hx is not None:
                for idx, value in enumerate(hx):
                    if value.dim() not in (1, 2):
                        raise ValueError(f"LSTMCell: Expected hx[{idx}] to be 1D or 2D, got {value.dim()}D instead")
            is_batched = input.dim() == 2
            if not is_batched:
                input = input.unsqueeze(0)

            if hx is None:
                zeros = torch.zeros(input.size(0), self.hidden_size, dtype=input.dtype, device=input.device)
                hx = (zeros, zeros)
            else:
                hx = (hx[0].unsqueeze(0), hx[1].unsqueeze(0)) if not is_batched else hx

            ret = _VF.lstm_cell(
                input, hx,
                self.weight_ih, self.weight_hh,
                self.bias_ih, self.bias_hh,
            )

            if not is_batched:
                ret = (ret[0].squeeze(0), ret[1].squeeze(0))
            return ret

    class GRUCell(RNNCellBase):
        r"""A gated recurrent unit (GRU) cell

        .. math::

            \begin{array}{ll}
            r = \sigma(W_{ir} x + b_{ir} + W_{hr} h + b_{hr}) \\
            z = \sigma(W_{iz} x + b_{iz} + W_{hz} h + b_{hz}) \\
            n = \tanh(W_{in} x + b_{in} + r * (W_{hn} h + b_{hn})) \\
            h' = (1 - z) * n + z * h
            \end{array}

        where :math:`\sigma` is the sigmoid function, and :math:`*` is the Hadamard product.

        Args:
            input_size: The number of expected features in the input `x`
            hidden_size: The number of features in the hidden state `h`
            bias: If ``False``, then the layer does not use bias weights `b_ih` and
                `b_hh`. Default: ``True``

        Inputs: input, hidden
            - **input** : tensor containing input features
            - **hidden** : tensor containing the initial hidden
              state for each element in the batch.
              Defaults to zero if not provided.

        Outputs: h'
            - **h'** : tensor containing the next hidden state
              for each element in the batch

        Shape:
            - input: :math:`(N, H_{in})` or :math:`(H_{in})` tensor containing input features where
              :math:`H_{in}` = `input_size`.
            - hidden: :math:`(N, H_{out})` or :math:`(H_{out})` tensor containing the initial hidden
              state where :math:`H_{out}` = `hidden_size`. Defaults to zero if not provided.
            - output: :math:`(N, H_{out})` or :math:`(H_{out})` tensor containing the next hidden state.

        Attributes:
            weight_ih: the learnable input-hidden weights, of shape
                `(3*hidden_size, input_size)`
            weight_hh: the learnable hidden-hidden weights, of shape
                `(3*hidden_size, hidden_size)`
            bias_ih: the learnable input-hidden bias, of shape `(3*hidden_size)`
            bias_hh: the learnable hidden-hidden bias, of shape `(3*hidden_size)`

        .. note::
            All the weights and biases are initialized from :math:`\mathcal{U}(-\sqrt{k}, \sqrt{k})`
            where :math:`k = \frac{1}{\text{hidden\_size}}`

        On certain ROCm devices, when using float16 inputs this module will use :ref:`different precision<fp16_on_mi200>` for backward.

        Examples::

            >>> rnn = nn.GRUCell(10, 20)
            >>> input = torch.randn(6, 3, 10)
            >>> hx = torch.randn(3, 20)
            >>> output = []
            >>> for i in range(6):
            ...     hx = rnn(input[i], hx)
            ...     output.append(hx)
        """

        def __init__(self, input_size: int, hidden_size: int, bias: bool = True,
                     device=None, dtype=None) -> None:
            factory_kwargs = {'device': device, 'dtype': dtype}
            super().__init__(input_size, hidden_size, bias, num_chunks=3, **factory_kwargs)

        def forward(self, input: Tensor, hx: Optional[Tensor] = None) -> Tensor:
            if input.dim() not in (1, 2):
                raise ValueError(f"GRUCell: Expected input to be 1D or 2D, got {input.dim()}D instead")
            if hx is not None and hx.dim() not in (1, 2):
                raise ValueError(f"GRUCell: Expected hidden to be 1D or 2D, got {hx.dim()}D instead")
            is_batched = input.dim() == 2
            if not is_batched:
                input = input.unsqueeze(0)

            if hx is None:
                hx = torch.zeros(input.size(0), self.hidden_size, dtype=input.dtype, device=input.device)
            else:
                hx = hx.unsqueeze(0) if not is_batched else hx

            ret = _VF.gru_cell(
                input, hx,
                self.weight_ih, self.weight_hh,
                self.bias_ih, self.bias_hh,
            )

            if not is_batched:
                ret = ret.squeeze(0)

            return ret

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