pyvene.models.mlp.modelings_mlp.MLPModelOutput

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

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