(self, c_in, c_out, dropout, support_len=2, order=1)

(y_pred: np.array, y_true: np.array)

(self, data: dict, params: dict)

(self, data: dict)

(self)

(self)

(self)

(self)

(preds, labels)

(self, inputs: dict, output: torch.Tensor)

(preds, labels)

(self, data_len: int)

(self, data_loader: dict, modes: list)

(self, data_loader: dict, modes: list, early_stop_patience=20)

(self, data)

(y_pred: np.array, y_true: np.array)

(y_pred: np.array, y_true: np.array)

(y_pred: np.array, y_true: np.array)

(y_pred: np.array, y_true: np.array)

(y_pred: np.array, y_true: np.array)

(self, item)

(self, data_dir: str, data_split_ratio: tuple)

(self, mean, std)

(self, inputs: dict, output: torch.Tensor, mode: str, mode_len: dict)

(self, obs_len: int, pred_len, data_split_ratio: tuple)

(self)

(self,c_in,c_out)

(self, num_nodes, dropout, in_dim, out_len, residual_channels, dilation_channels, skip_channels,

(self)

(self, num_nodes, adpinit, glm_type, dropout=0.5, in_dim=4, in_len=14, out_len=14, residual_channels=32,

(self, params: dict, data: dict, data_container)

(self)

(self,x, A)

(self, x)

(self, x, support)

(self, input, adp_g)

(self, param_b: torch.Tensor, param_g: torch.Tensor, mob: torch.Tensor, SIR: torch.Tensor)

(self, x_node, SIR, od, max_od)

(self, data)