diff --git a/faiss/utils/NeuralNet.cpp b/faiss/utils/NeuralNet.cpp
index 9d5465bae8..9d25b24c4c 100644
--- a/faiss/utils/NeuralNet.cpp
+++ b/faiss/utils/NeuralNet.cpp
@@ -212,19 +212,19 @@ nn::Int32Tensor2D QINCoStep::encode(
     // repeated codebook
     Tensor2D zqs_r(n * K, d);  // size n, K, d
     Tensor2D cc(n * K, d * 2); // size n, K, d * 2
-    size_t d = this->d;
+    size_t d_2 = this->d;
 
-    auto copy_row = [d](Tensor2D& t, size_t i, size_t j, const float* data) {
+    auto copy_row = [d_2](Tensor2D& t, size_t i, size_t j, const float* data) {
         assert(i <= t.shape[0] && j <= t.shape[1]);
-        memcpy(t.data() + i * t.shape[1] + j, data, sizeof(float) * d);
+        memcpy(t.data() + i * t.shape[1] + j, data, sizeof(float) * d_2);
     };
 
     // manual broadcasting
     for (size_t i = 0; i < n; i++) {
         for (size_t j = 0; j < K; j++) {
-            copy_row(zqs_r, i * K + j, 0, codebook.data() + j * d);
-            copy_row(cc, i * K + j, 0, codebook.data() + j * d);
-            copy_row(cc, i * K + j, d, xhat.data() + i * d);
+            copy_row(zqs_r, i * K + j, 0, codebook.data() + j * d_2);
+            copy_row(cc, i * K + j, 0, codebook.data() + j * d_2);
+            copy_row(cc, i * K + j, d_2, xhat.data() + i * d_2);
         }
     }
 
@@ -237,13 +237,13 @@ nn::Int32Tensor2D QINCoStep::encode(
 
     // add the xhat
     for (size_t i = 0; i < n; i++) {
-        float* zqs_r_row = zqs_r.data() + i * K * d;
-        const float* xhat_row = xhat.data() + i * d;
+        float* zqs_r_row = zqs_r.data() + i * K * d_2;
+        const float* xhat_row = xhat.data() + i * d_2;
         for (size_t l = 0; l < K; l++) {
-            for (size_t j = 0; j < d; j++) {
+            for (size_t j = 0; j < d_2; j++) {
                 zqs_r_row[j] += xhat_row[j];
             }
-            zqs_r_row += d;
+            zqs_r_row += d_2;
         }
     }
 
@@ -252,31 +252,31 @@ nn::Int32Tensor2D QINCoStep::encode(
     float* res = nullptr;
     if (residuals) {
         FAISS_THROW_IF_NOT(
-                residuals->shape[0] == n && residuals->shape[1] == d);
+                residuals->shape[0] == n && residuals->shape[1] == d_2);
         res = residuals->data();
     }
 
     for (size_t i = 0; i < n; i++) {
-        const float* q = x.data() + i * d;
-        const float* db = zqs_r.data() + i * K * d;
+        const float* q = x.data() + i * d_2;
+        const float* db = zqs_r.data() + i * K * d_2;
         float dis_min = HUGE_VALF;
         int64_t idx = -1;
         for (size_t j = 0; j < K; j++) {
-            float dis = fvec_L2sqr(q, db, d);
+            float dis = fvec_L2sqr(q, db, d_2);
             if (dis < dis_min) {
                 dis_min = dis;
                 idx = j;
             }
-            db += d;
+            db += d_2;
         }
         codes.v[i] = idx;
         if (res) {
-            const float* xhat_row = xhat.data() + i * d;
-            const float* xhat_next_row = zqs_r.data() + (i * K + idx) * d;
-            for (size_t j = 0; j < d; j++) {
+            const float* xhat_row = xhat.data() + i * d_2;
+            const float* xhat_next_row = zqs_r.data() + (i * K + idx) * d_2;
+            for (size_t j = 0; j < d_2; j++) {
                 res[j] = xhat_next_row[j] - xhat_row[j];
             }
-            res += d;
+            res += d_2;
         }
     }
     return codes;