Add a Context trait similar to anyhow::Context. (#2676)

* Add a Context trait similar to anyhow::Context.

* Switch two unwrap to context.

1 files changed, 2 insertions, 2 deletions

diff --git a/candle-transformers/src/models/chinese_clip/vision_model.rs b/candle-transformers/src/models/chinese_clip/vision_model.rs
index a20535c4..153fe833 100644
--- a/candle-transformers/src/models/chinese_clip/vision_model.rs
+++ b/candle-transformers/src/models/chinese_clip/vision_model.rs
@@ -6,7 +6,7 @@
 //! - 💻 [Chinese-CLIP](https://github.com/OFA-Sys/Chinese-CLIP)
 //! - 💻 [GH](https://github.com/huggingface/transformers/blob/5af7d41e49bbfc8319f462eb45253dcb3863dfb7/src/transformers/models/chinese_clip/modeling_chinese_clip.py_
 
-use candle::{DType, IndexOp, Module, Result, Shape, Tensor, D};
+use candle::{Context, DType, IndexOp, Module, Result, Shape, Tensor, D};
 use candle_nn as nn;
 
 use super::{Activation, EncoderConfig};
@@ -363,7 +363,7 @@ impl ChineseClipVisionTransformer {
             .apply(&self.pre_layer_norm)?;
 
         let mut result = self.encoder.output_hidden_states(&hidden_states, None)?;
-        let encoder_outputs = result.last().unwrap();
+        let encoder_outputs = result.last().context("no last")?;
         let pooled_output = encoder_outputs.i((.., 0, ..))?;
         result.push(self.final_layer_norm.forward(&pooled_output)?.clone());
         Ok(result)