dlrm_data_caffe2.py [153:168]:
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        if self.max_ind_range > 0:
            return self.X_int[i], self.X_cat[i] % self.max_ind_range, self.y[i]
        else:
            return self.X_int[i], self.X_cat[i], self.y[i]

    def _default_preprocess(self, X_int, X_cat, y):
        X_int = torch.log(torch.tensor(X_int, dtype=torch.float) + 1)
        if self.max_ind_range > 0:
            X_cat = torch.tensor(X_cat % self.max_ind_range, dtype=torch.long)
        else:
            X_cat = torch.tensor(X_cat, dtype=torch.long)
        y = torch.tensor(y.astype(np.float32))

        return X_int, X_cat, y

    def __len__(self):
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dlrm_data_pytorch.py [297:312]:
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        if self.max_ind_range > 0:
            return self.X_int[i], self.X_cat[i] % self.max_ind_range, self.y[i]
        else:
            return self.X_int[i], self.X_cat[i], self.y[i]

    def _default_preprocess(self, X_int, X_cat, y):
        X_int = torch.log(torch.tensor(X_int, dtype=torch.float) + 1)
        if self.max_ind_range > 0:
            X_cat = torch.tensor(X_cat % self.max_ind_range, dtype=torch.long)
        else:
            X_cat = torch.tensor(X_cat, dtype=torch.long)
        y = torch.tensor(y.astype(np.float32))

        return X_int, X_cat, y

    def __len__(self):
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