cCiF zSrSSKrSSKJr SSKJrJr SSKrSSKJr SSK J r SSK J r SS K Jr SS KJrJr SS KJrJrJrJrJrJrJrJrJr SS KJr S SKJr \"5(a SSK J!r!J"r" OSr"Sr!\RF"\$5r%Sr&Sr'/SQr(Sr)Sr*\"SS\55r+\"SS\55r,\"SS\55r-"SS\R\5r/"SS\R\5r0"S S!\R\5r1SGS"\RdS#\3S$\4S%\Rd4S&jjr5"S'S(\R\5r6"S)S*\R\5r7"S+S,\R\5r8"S-S.\R\5r9"S/S0\R\5r:"S1S2\R\5r;"S3S4\R\5r<"S5S6\R\5r="S7S8\R\5r>"S9S:\5r?S;r@SS?\?55rB\"S@\@5"SASB\?55rC\"SC\@5"SDSE\?\55rD/SFQrEg)Hz1PyTorch Neighborhood Attention Transformer model.N) dataclass)OptionalUnion)nn)ACT2FN)BackboneOutput)PreTrainedModel) find_pruneable_heads_and_indicesprune_linear_layer) ModelOutputOptionalDependencyNotAvailableadd_code_sample_docstringsadd_start_docstrings%add_start_docstrings_to_model_forwardis_natten_availableloggingreplace_return_docstringsrequires_backends) BackboneMixin) NatConfig) natten2dav natten2dqkrpbc[5eNrargskwargss i/home/james-whalen/.local/lib/python3.13/site-packages/transformers/models/deprecated/nat/modeling_nat.pyrr/ ,..c[5errrs r!rr2r"r#rzshi-labs/nat-mini-in1k-224)rr%iz tiger catc\rSrSr%SrSr\\R\ S'Sr \\ \RS4\ S'Sr \\ \RS4\ S'Sr \\ \RS4\ S'S rg) NatEncoderOutputGa3 Nat encoder's outputs, with potential hidden states and attentions. Args: last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`): Sequence of hidden-states at the output of the last layer of the model. hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each stage) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer plus the initial embedding outputs. attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `torch.FloatTensor` (one for each stage) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. reshaped_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each stage) of shape `(batch_size, hidden_size, height, width)`. Hidden-states of the model at the output of each layer plus the initial embedding outputs reshaped to include the spatial dimensions. Nlast_hidden_state. hidden_states attentionsreshaped_hidden_states)__name__ __module__ __qualname____firstlineno____doc__r)rtorch FloatTensor__annotations__r*tupler+r,__static_attributes__r-r#r!r'r'Gs}26:x 1 129=AM8E%"3"3S"89:A:>Ju00#567>FJHU5+<+