pyvene.models.mlp.modelings_mlp.MLPModelOutput

pyvene.models.mlp.modelings_mlp.MLPModelOutput#

class MLPModelOutput(last_hidden_state: torch.FloatTensor = None, hidden_states: Tuple[torch.FloatTensor] | None = None)[source]#

Bases: ModelOutput

__init__(last_hidden_state: FloatTensor | None = None, hidden_states: Tuple[FloatTensor] | None = None) None#

Methods

__init__([last_hidden_state, hidden_states])

clear()

copy()

fromkeys([value])

Create a new ordered dictionary with keys from iterable and values set to value.

get(key[, default])

Return the value for key if key is in the dictionary, else default.

items()

keys()

move_to_end(key[, last])

Move an existing element to the end (or beginning if last is false).

pop(key[,default])

If the key is not found, return the default if given; otherwise, raise a KeyError.

popitem([last])

Remove and return a (key, value) pair from the dictionary.

setdefault(*args, **kwargs)

Insert key with a value of default if key is not in the dictionary.

to_tuple()

Convert self to a tuple containing all the attributes/keys that are not None.

update([E, ]**F)

If E is present and has a .keys() method, then does: for k in E: D[k] = E[k] If E is present and lacks a .keys() method, then does: for k, v in E: D[k] = v In either case, this is followed by: for k in F: D[k] = F[k]

values()

Attributes

hidden_states

last_hidden_state