custom service.md link fixes

docs/custom_service.md

@@ -250,7 +250,7 @@ class ModelHandler(BaseHandler):
 
 ```
 
-Refer [waveglow_handler](../examples/text_to_speech_synthesizer/waveglow_handler.py) for more details.
+Refer [waveglow_handler](https://github.com/pytorch/serve/blob/master/examples/text_to_speech_synthesizer/waveglow_handler.py) for more details.
 
 #### Captum explanations for custom handler
 
@@ -331,12 +331,12 @@ The above ModelHandler class should have the following methods with captum funct
 #### Extend default handlers
 
 TorchServe has following default handlers.
-- [image_classifier](../ts/torch_handler/image_classifier.py)
-- [image_segmenter](../ts/torch_handler/image_segmenter.py)
-- [object_detector](../ts/torch_handler/object_detector.py)
-- [text_classifier](../ts/torch_handler/text_classifier.py)
+- [image_classifier](https://github.com/pytorch/serve/blob/master/ts/torch_handler/image_classifier.py)
+- [image_segmenter](https://github.com/pytorch/serve/blob/master/ts/torch_handler/image_segmenter.py)
+- [object_detector](https://github.com/pytorch/serve/blob/master/ts/torch_handler/object_detector.py)
+- [text_classifier](https://github.com/pytorch/serve/blob/master/ts/torch_handler/text_classifier.py)
 
-If required above handlers can be extended to create custom handler. Also, you can extend abstract [base_handler](../ts/torch_handler/base_handler.py).
+If required above handlers can be extended to create custom handler. Also, you can extend abstract [base_handler](https://github.com/pytorch/serve/blob/master/ts/torch_handler/base_handler.py).
 
 To import the default handler in a python script use the following import statement.
 
@@ -360,16 +360,16 @@ class CustomImageClassifier(ImageClassifier):
 
 ```
 For more details refer following examples :
-- [mnist digit classifier handler](../examples/image_classifier/mnist/mnist_handler.py)
-- [Huggingface transformer generalized handler](../examples/Huggingface_Transformers/Transformer_handler_generalized.py)
-- [Waveglow text to speech synthesizer](../examples/text_to_speech_synthesizer/waveglow_handler.py)
+- [mnist digit classifier handler](https://github.com/pytorch/serve/tree/master/examples/image_classifier)
+- [Huggingface transformer generalized handler](https://github.com/pytorch/serve/blob/master/examples/Huggingface_Transformers/Transformer_handler_generalized.py)
+- [Waveglow text to speech synthesizer](https://github.com/pytorch/serve/blob/master/examples/text_to_speech_synthesizer/waveglow_handler.py)
 
 ## Creating a model archive with an entry point
 
 TorchServe identifies the entry point to the custom service from a manifest file.
 When you create the model archive, specify the location of the entry point by using the `--handler` option.
 
-The [model-archiver](../model-archiver/README.md) tool enables you to create a model archive that TorchServe can serve.
+The [model-archiver](https://github.com/pytorch/serve/tree/master/model-archiver) tool enables you to create a model archive that TorchServe can serve.
 
 ```bash
 torch-model-archiver --model-name <model-name> --version <model_version_number> --handler model_handler[:<entry_point_function_name>] [--model-file <path_to_model_architecture_file>] --serialized-file <path_to_state_dict_file> [--extra-files <comma_seperarted_additional_files>] [--export-path <output-dir> --model-path <model_dir>] [--runtime python3]
@@ -415,5 +415,5 @@ Custom models/handlers may depend on different python packages which are not ins
 
 Following steps allows user to supply a list of custom python packages to be installed by `TorchServe` for seamless model serving.
 
-1) [Enable model specific python package installation](configuration.md#allow-model-specific-custom-python-packages)
-2) [Supply a requirements file with the model-archive](../model-archiver/README.md#torch-model-archiver-command-line-interface).
+1) [Enable model specific python package installation](https://pytorch.org/serve/configuration.html#allow-model-specific-custom-python-packages)
+2) [Supply a requirements file with the model-archive](https://github.com/pytorch/serve/tree/master/model-archiver#torch-model-archiver-command-line-interface).