src/transformers/models/informer/modeling_informer.py/InformerModel/__init__
class InformerModel:    def __init__(self, config: InformerConfig):
        super().__init__(config)

        if config.scaling == "mean" or config.scaling:
            self.scaler = InformerMeanScaler(dim=1, keepdim=True)
        elif config.scaling == "std":
            self.scaler = InformerStdScaler(dim=1, keepdim=True)
        else:
            self.scaler = InformerNOPScaler(dim=1, keepdim=True)

        if config.num_static_categorical_features > 0:
            self.embedder = InformerFeatureEmbedder(
                cardinalities=config.cardinality,
                embedding_dims=config.embedding_dimension,
            )

        # transformer encoder-decoder and mask initializer
        self.encoder = InformerEncoder(config)
        self.decoder = InformerDecoder(config)

        # Initialize weights and apply final processing
        self.post_init()