tzrec/features/custom_feature.py [44:119]:
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    def __init__(
        self,
        feature_config: FeatureConfig,
        fg_mode: FgMode = FgMode.FG_NONE,
        fg_encoded_multival_sep: Optional[str] = None,
    ) -> None:
        super().__init__(feature_config, fg_mode, fg_encoded_multival_sep)

    @property
    def name(self) -> str:
        """Feature name."""
        return self.config.feature_name

    # pyre-ignore [56]
    @BaseFeature.is_neg.setter
    def is_neg(self, value: bool) -> None:
        """Feature is negative sampled or not."""
        self._is_neg = value
        self._data_group = CROSS_NEG_DATA_GROUP

    @property
    def value_dim(self) -> int:
        """Fg value dimension of the feature."""
        if self.config.HasField("value_dim"):
            return self.config.value_dim
        else:
            return 1

    @property
    def output_dim(self) -> int:
        """Output dimension of the feature after embedding."""
        if self.has_embedding:
            return self.config.embedding_dim
        else:
            return max(self.config.value_dim, 1)

    @property
    def is_sparse(self) -> bool:
        """Feature is sparse or dense."""
        if self._is_sparse is None:
            self._is_sparse = (
                self.config.HasField("zch")
                or self.config.HasField("hash_bucket_size")
                or self.config.HasField("num_buckets")
                or len(self.vocab_list) > 0
                or len(self.vocab_dict) > 0
                or len(self.vocab_file) > 0
                or len(self.config.boundaries) > 0
            )
        return self._is_sparse

    @property
    def num_embeddings(self) -> int:
        """Get embedding row count."""
        if self.config.HasField("zch"):
            num_embeddings = self.config.zch.zch_size
        elif self.config.HasField("hash_bucket_size"):
            num_embeddings = self.config.hash_bucket_size
        elif self.config.HasField("num_buckets"):
            num_embeddings = self.config.num_buckets
        elif len(self.vocab_list) > 0:
            num_embeddings = len(self.vocab_list)
        elif len(self.vocab_dict) > 0:
            num_embeddings = max(list(self.vocab_dict.values())) + 1
        elif len(self.vocab_file) > 0:
            self.init_fg()
            num_embeddings = self._fg_op.vocab_list_size()
        else:
            num_embeddings = len(self.config.boundaries) + 1
        return num_embeddings

    @property
    def _dense_emb_type(self) -> Optional[str]:
        return self.config.WhichOneof("dense_emb")

    def _build_side_inputs(self) -> Optional[List[Tuple[str, str]]]:
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tzrec/features/lookup_feature.py [42:117]:
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    def __init__(
        self,
        feature_config: FeatureConfig,
        fg_mode: FgMode = FgMode.FG_NONE,
        fg_encoded_multival_sep: Optional[str] = None,
    ) -> None:
        super().__init__(feature_config, fg_mode, fg_encoded_multival_sep)

    @property
    def name(self) -> str:
        """Feature name."""
        return self.config.feature_name

    # pyre-ignore [56]
    @BaseFeature.is_neg.setter
    def is_neg(self, value: bool) -> None:
        """Feature is negative sampled or not."""
        self._is_neg = value
        self._data_group = CROSS_NEG_DATA_GROUP

    @property
    def value_dim(self) -> int:
        """Fg value dimension of the feature."""
        if self.config.HasField("value_dim"):
            return self.config.value_dim
        else:
            return 1

    @property
    def output_dim(self) -> int:
        """Output dimension of the feature after embedding."""
        if self.has_embedding:
            return self.config.embedding_dim
        else:
            return max(self.config.value_dim, 1)

    @property
    def is_sparse(self) -> bool:
        """Feature is sparse or dense."""
        if self._is_sparse is None:
            self._is_sparse = (
                self.config.HasField("zch")
                or self.config.HasField("hash_bucket_size")
                or self.config.HasField("num_buckets")
                or len(self.vocab_list) > 0
                or len(self.vocab_dict) > 0
                or len(self.vocab_file) > 0
                or len(self.config.boundaries) > 0
            )
        return self._is_sparse

    @property
    def num_embeddings(self) -> int:
        """Get embedding row count."""
        if self.config.HasField("zch"):
            num_embeddings = self.config.zch.zch_size
        elif self.config.HasField("hash_bucket_size"):
            num_embeddings = self.config.hash_bucket_size
        elif self.config.HasField("num_buckets"):
            num_embeddings = self.config.num_buckets
        elif len(self.vocab_list) > 0:
            num_embeddings = len(self.vocab_list)
        elif len(self.vocab_dict) > 0:
            num_embeddings = max(list(self.vocab_dict.values())) + 1
        elif len(self.vocab_file) > 0:
            self.init_fg()
            num_embeddings = self._fg_op.vocab_list_size()
        else:
            num_embeddings = len(self.config.boundaries) + 1
        return num_embeddings

    @property
    def _dense_emb_type(self) -> Optional[str]:
        return self.config.WhichOneof("dense_emb")

    def _build_side_inputs(self) -> Optional[List[Tuple[str, str]]]:
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