Source code for cuvis_ai.deciders.cascaded_decider

from .base_decider import BaseDecider

import numpy as np
from typing import Dict


[docs] class Cascaded(BaseDecider):
[docs] def __init__(self) -> None: super().__init__()
[docs] def forward(self, X: np.ndarray) -> np.ndarray: pass
@BaseDecider.input_dim.getter def input_dim(self): return [-1, -1, 1] @BaseDecider.output_dim.getter def output_dim(self): """ Returns the provided shape for the output data. If a dimension is not important it will return -1 in the specific position. Returns ------- tuple Provided shape for data """ return [-1, -1, 1]
[docs] def serialize(self, directory: str): """ Convert the class into a serialized representation """ data = { } return data
[docs] def load(self, params: dict, filepath: str): """Load this node from a serialized graph.""" pass # No attributes