Fixing examples.

candle-book/src/inference/hub.md

@@ -58,20 +58,20 @@ Now that we have our weights, we can use them in our bert architecture:
 # extern crate candle_nn;
 # extern crate hf_hub;
 # use hf_hub::api::sync::Api;
-# use candle::Device;
 # 
 # let api = Api::new().unwrap();
 # let repo = api.model("bert-base-uncased".to_string());
 # 
 # let weights = repo.get("model.safetensors").unwrap();
+use candle::{Device, Tensor, DType};
 use candle_nn::Linear;
 
-let weights = candle::safetensors::load(weights, &Device::Cpu);
+let weights = candle::safetensors::load(weights, &Device::Cpu).unwrap();
 
 let weight = weights.get("bert.encoder.layer.0.attention.self.query.weight").unwrap();
 let bias = weights.get("bert.encoder.layer.0.attention.self.query.bias").unwrap();
 
-let linear = Linear::new(weight, Some(bias));
+let linear = Linear::new(weight.clone(), Some(bias.clone()));
 
 let input_ids = Tensor::zeros((3, 7680), DType::F32, &Device::Cpu).unwrap();
 let output = linear.forward(&input_ids);