Train the imputer on the given data. Warnings -------- LOCF does not need to run fit(). Please run func ``predict()``

Categorical variables as defined in input data. Returns: List[str]: list of variables

Network forward pass. Args: x (Dict[str, Union[torch.Tensor, List[torch.Tensor]]]): network input (x as returned by the

input dimensions: n_samples x time x variables

Forward processing of the NN module. Parameters ---------- x : tensor, the input for processing Returns

Parameters ---------- inputs : Input data, a dictionary includes feature values, missing masks, and time-gap valu

Forward processing of this NN module. Parameters ---------- delta : tensor, shape [n_samples, n_steps, n_features]

Forward processing of GRU-D. Parameters ---------- X: missing_mask: deltas: empirical_mean:

:param inputs: shape: (batch, seq_len, dim) :return: shape: (batch, seq_len, dim)

x: (b, t, d)

Generate positional encoding. Parameters ---------- time_vectors : tensor, Tensor embeds time information.

r""" Args: return_attention_weights (bool, optional): If set to :obj:`True`, will additionally return the tuple

Forward processing of BRITS. Parameters ---------- X : The input tensor of shape (batch_size, n_features, max_len

x: [bs x nvars x d_model x num_patch] output: [bs x output_dim]

x: [bs x nvars x d_model x num_patch] output: [bs x n_classes]

x: [bs x nvars x d_model x num_patch] output: [bs x forecast_len x nvars]

Forward processing of USGAN Discriminator. Parameters ---------- imputed_X : torch.Tensor, The original X with mi