Merge pull request #127 from dil-bhantos/main

Update README.md

README.md

@@ -150,7 +150,7 @@ model.tar.gz/
 In this example, `pytorch_model.bin` is the model file saved from training, `inference.py` is the custom inference module, and `requirements.txt` is a requirements file to add additional dependencies.
 The custom module can override the following methods:  
 
-* `model_fn(model_dir)`: overrides the default method for loading the model, the return value `model` will be used in the `predict()` for predicitions. It receives argument the `model_dir`, the path to your unzipped `model.tar.gz`.
+* `model_fn(model_dir, context=None)`: overrides the default method for loading the model, the return value `model` will be used in the `predict()` for predicitions. It receives argument the `model_dir`, the path to your unzipped `model.tar.gz`.
 * `transform_fn(model, data, content_type, accept_type)`: overrides the default transform function with a custom implementation. Customers using this would have to implement `preprocess`, `predict` and `postprocess` steps in the `transform_fn`. **NOTE: This method can't be combined with `input_fn`, `predict_fn` or `output_fn` mentioned below.** 
 * `input_fn(input_data, content_type)`: overrides the default method for preprocessing, the return value `data` will be used in the `predict()` method for predicitions. The input is `input_data`, the raw body of your request and `content_type`, the content type form the request Header.
 * `predict_fn(processed_data, model)`: overrides the default method for predictions, the return value `predictions` will be used in the `postprocess()` method. The input is `processed_data`, the result of the `preprocess()` method.
@@ -239,4 +239,4 @@ curl --request POST \
   --url http://localhost:8080/invocations \
   --header 'Content-Type: application/json' \
   --data "{\"inputs\": \"I like you.\"}"
-```
+```