eTh٤SSKJrJrJr SSKrSSKJr SSKJ r \RR"SS\ 55r \RR"SS\ 55r \RR"S S \ 55r\RR"S S \ 55r\RR"S S\ 55r\RR"SS\ 55r\RR"SS\ 55r\RR"SS\ 55r\RR"SS\ 55r\RR"SS\ 55r\RR"SS\ 55r\r\RR"SS\ 55r\RR"SS\ 55r\RR"SS \ 55r\RR"S!S"\ 55r\RR"S#S$\ 55r\RR"S%S&\ 55r\RR"S'S(\ 55r\RR"S)S*\ 55rg)+)DictOptionalTupleN) ModelOutputc\rSrSr%SrSr\\R\ S'Sr \\ \R\ S'Sr \\ \R\ S'Sr g)FlaxBaseModelOutputa6 Base class for model's outputs, with potential hidden states and attentions. Args: last_hidden_state (`jnp.ndarray` 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(jnp.ndarray)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `jnp.ndarray` (one for the output of the embeddings + one for the output of each layer) 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(jnp.ndarray)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `jnp.ndarray` (one for each layer) 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. Nlast_hidden_state hidden_states attentions)__name__ __module__ __qualname____firstlineno____doc__r rjnpndarray__annotations__r rr __static_attributes__rZ/var/www/auris/envauris/lib/python3.13/site-packages/transformers/modeling_flax_outputs.pyr r sM&04x ,326M8E#++./6/3Js{{+,3rr cp\rSrSr%SrSr\\R\ S'Sr \\ \R\ S'Sr g)"FlaxBaseModelOutputWithNoAttention0a Base class for model's outputs, with potential hidden states. Args: last_hidden_state (`jnp.ndarray` of shape `(batch_size, num_channels, height, width)`): Sequence of hidden-states at the output of the last layer of the model. hidden_states (`tuple(jnp.ndarray)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `jnp.ndarray` (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each layer) of shape `(batch_size, num_channels, height, width)`. Hidden-states of the model at the output of each layer plus the optional initial embedding outputs. Nr r r) rrrrrr rrrrr rrrrrrr0s5 04x ,326M8E#++./6rrc\rSrSr%SrSr\\R\ S'Sr \\R\ S'Sr \\ \R\ S'Sr g),FlaxBaseModelOutputWithPoolingAndNoAttentionBaw Base class for model's outputs that also contains a pooling of the last hidden states. Args: last_hidden_state (`jnp.ndarray` of shape `(batch_size, num_channels, height, width)`): Sequence of hidden-states at the output of the last layer of the model. pooler_output (`jnp.ndarray` of shape `(batch_size, hidden_size)`): Last layer hidden-state after a pooling operation on the spatial dimensions. hidden_states (`tuple(jnp.ndarray)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `jnp.ndarray` (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each layer) of shape `(batch_size, num_channels, height, width)`. Hidden-states of the model at the output of each layer plus the optional initial embedding outputs. Nr pooler_outputr r)rrrrrr rrrrr r rrrrrrrBsH 04x ,3+/M8CKK(/26M8E#++./6rrcp\rSrSr%SrSr\\R\ S'Sr \\ \R\ S'Sr g)(FlaxImageClassifierOutputWithNoAttentionWa Base class for outputs of image classification models. Args: logits (`jnp.ndarray` of shape `(batch_size, config.num_labels)`): Classification (or regression if config.num_labels==1) scores (before SoftMax). hidden_states (`tuple(jnp.ndarray)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `jnp.ndarray` (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each stage) of shape `(batch_size, num_channels, height, width)`. Hidden-states (also called feature maps) of the model at the output of each stage. Nlogitsr r) rrrrrr$rrrrr rrrrrr"r"Ws4 %)FHS[[ !(26M8E#++./6rr"c\rSrSr%SrSr\\R\ S'Sr \\ \ \R4\ S'Sr \\\R\ S'Sr\\\R\ S'Srg) FlaxBaseModelOutputWithPastjaN Base class for model's outputs, with potential hidden states and attentions. Args: last_hidden_state (`jnp.ndarray` of shape `(batch_size, sequence_length, hidden_size)`): Sequence of hidden-states at the output of the last layer of the model. past_key_values (`Dict[str, jnp.ndarray]`): Dictionary of pre-computed hidden-states (key and values in the attention blocks) that can be used for fast auto-regressive decoding. Pre-computed key and value hidden-states are of shape *[batch_size, max_length]*. hidden_states (`tuple(jnp.ndarray)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `jnp.ndarray` (one for the output of the embeddings + one for the output of each layer) 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(jnp.ndarray)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `jnp.ndarray` (one for each layer) 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. Nr past_key_valuesr r r)rrrrrr rrrrr(rstrr rr rrrrr&r&jsj,04x ,38