tabular/src/autogluon/tabular/models/tabular_nn/mxnet/tabular_nn_dataset.py [171:190]:
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        else:
            return None

    def getNumCategoriesEmbeddings(self):
        """ Returns number of categories for each embedding feature.
            Should only be applied to training data.
            If training data feature contains unique levels 1,...,n-1, there are actually n categories,
            since category n is reserved for unknown test-time categories.
        """
        if self.num_categories_per_embed_feature is not None:
            return self.num_categories_per_embedfeature
        else:
            num_embed_feats = self.num_embed_features()
            num_categories_per_embedfeature = [0] * num_embed_feats
            for i in range(num_embed_feats):
                feat_i = self.feature_groups['embed'][i]
                feat_i_data = self.get_feature_data(feat_i).flatten().tolist()
                num_categories_i = len(set(feat_i_data)) # number of categories for ith feature
                num_categories_per_embedfeature[i] = num_categories_i + 1 # to account for unknown test-time categories
            return num_categories_per_embedfeature
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tabular/src/autogluon/tabular/models/tabular_nn/torch/tabular_torch_dataset.py [156:175]:
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        else:
            return None

    def getNumCategoriesEmbeddings(self):
        """ Returns number of categories for each embedding feature.
            Should only be applied to training data.
            If training data feature contains unique levels 1,...,n-1, there are actually n categories,
            since category n is reserved for unknown test-time categories.
        """
        if self.num_categories_per_embed_feature is not None:
            return self.num_categories_per_embedfeature
        else:
            num_embed_feats = self.num_embed_features()
            num_categories_per_embedfeature = [0] * num_embed_feats
            for i in range(num_embed_feats):
                feat_i = self.feature_groups['embed'][i]
                feat_i_data = self.get_feature_data(feat_i).flatten().tolist()
                num_categories_i = len(set(feat_i_data)) # number of categories for ith feature
                num_categories_per_embedfeature[i] = num_categories_i + 1 # to account for unknown test-time categories
            return num_categories_per_embedfeature
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