diff --git a/candle-examples/examples/segment-anything/model_image_encoder.rs b/candle-examples/examples/segment-anything/model_image_encoder.rs
index f5db2830..79e52d47 100644
--- a/candle-examples/examples/segment-anything/model_image_encoder.rs
+++ b/candle-examples/examples/segment-anything/model_image_encoder.rs
@@ -123,7 +123,7 @@ fn get_rel_pos(q_size: usize, k_size: usize, rel_pos: &Tensor) -> Result<Tensor>
     let relative_coords = relative_coords.to_dtype(DType::U32)?;
     rel_pos_resized
         .index_select(&relative_coords.reshape(d1 * d2)?, 0)?
-        .reshape((d1, d2, rel_pos_resized.dim(1)?))
+        .reshape((d1, d2, ()))
 }
 
 impl Module for Attention {
@@ -243,7 +243,7 @@ fn window_unpartition(
         ))?
         .transpose(2, 3)?
         .contiguous()?
-        .reshape((b, h_p, w_p, windows.elem_count() / b / h_p / w_p))?;
+        .reshape((b, h_p, w_p, ()))?;
     let xs = if h_p > h { xs.narrow(1, 0, h)? } else { xs };
     let xs = if w_p > w { xs.narrow(2, 0, w)? } else { xs };
     Ok(xs)
diff --git a/candle-examples/examples/segment-anything/model_mask_decoder.rs b/candle-examples/examples/segment-anything/model_mask_decoder.rs
index 1ef46eeb..acbfeeea 100644
--- a/candle-examples/examples/segment-anything/model_mask_decoder.rs
+++ b/candle-examples/examples/segment-anything/model_mask_decoder.rs
@@ -214,7 +214,7 @@ impl MaskDecoder {
         let hyper_in = Tensor::stack(hyper_in_list.as_slice(), 1)?;
         let (b, c, h, w) = upscaled_embedding.dims4()?;
         let masks = hyper_in.matmul(&upscaled_embedding.reshape((b, c, h * w))?)?;
-        let masks = masks.reshape((b, masks.elem_count() / b / h / w, h, w))?;
+        let masks = masks.reshape((b, (), h, w))?;
 
         // Generate mask quality predictions.
         let iou_pred = self.iou_prediction_head.forward(&iou_token_out)?;
diff --git a/candle-examples/examples/segment-anything/model_prompt_encoder.rs b/candle-examples/examples/segment-anything/model_prompt_encoder.rs
index c6ffffd2..aab0c4fd 100644
--- a/candle-examples/examples/segment-anything/model_prompt_encoder.rs
+++ b/candle-examples/examples/segment-anything/model_prompt_encoder.rs
@@ -28,10 +28,10 @@ impl PostionEmbeddingRandom {
         let x_embed = (Tensor::arange(0u32, w as u32, device)?.to_dtype(DType::F32)? + 0.5)?;
         let y_embed = (Tensor::arange(0u32, h as u32, device)?.to_dtype(DType::F32)? + 0.5)?;
         let x_embed = (x_embed / w as f64)?
-            .reshape((1, w))?
+            .reshape((1, ()))?
             .broadcast_as((h, w))?;
         let y_embed = (y_embed / h as f64)?
-            .reshape((h, 1))?
+            .reshape(((), 1))?
             .broadcast_as((h, w))?;
         let coords = Tensor::stack(&[&x_embed, &y_embed], D::Minus1)?;
         self.pe_encoding(&coords)?.permute((2, 0, 1))
@@ -163,7 +163,7 @@ impl PromptEncoder {
 
     fn embed_boxes(&self, boxes: &Tensor) -> Result<Tensor> {
         let boxes = (boxes + 0.5)?;
-        let coords = boxes.reshape((boxes.elem_count() / 4, 2, 2))?;
+        let coords = boxes.reshape(((), 2, 2))?;
         let corner_embedding = self
             .pe_layer
             .forward_with_coords(&coords, self.input_image_size)?;
@@ -200,7 +200,7 @@ impl PromptEncoder {
         let dense_embeddings = match masks {
             None => {
                 let emb = self.no_mask_embed.embeddings();
-                emb.reshape((1, emb.elem_count(), 1, 1))?.expand((
+                emb.reshape((1, (), 1, 1))?.expand((
                     1,
                     emb.elem_count(),
                     self.image_embedding_size.0,