Rename and refactoring.

README.md

@@ -24,7 +24,7 @@ git clone https://github.com/nod-ai/SHARK.git
 
 ### Run a demo script
 ```shell
-python -m  shark.examples.resnet50_script --device="cpu" # Use gpu | vulkan
+python -m  shark.examples.shark_inference.resnet50_script --device="cpu" # Use gpu | vulkan
 ```
 
 
@@ -43,16 +43,42 @@ pytest --workers auto
 from shark_runner import SharkInference
 
 shark_module = SharkInference(
-        module = torch.nn.module class.
+        module = model class.
         (input,)  = inputs to model (must be a torch-tensor)
         dynamic (boolean) = Pass the input shapes as static or dynamic.
         device = `cpu`, `gpu` or `vulkan` is supported.
         tracing_required = (boolean) = Jit trace the module with the given input, useful in the case where jit.script doesn't work. )
+shark_module.set_frontend("pytorch") # Use tensorflow, mhlo, linalg, tosa
+shark_module.compile()
 
 result = shark_module.forward(inputs)
 ```
 
 
+### Example demonstrating running MHLO IR.
+
+```
+from shark.shark_inference import SharkInference
+import numpy as np
+
+mhlo_ir = r"""builtin.module  {
+      func.func @forward(%arg0: tensor<1x4xf32>, %arg1: tensor<4x1xf32>) -> tensor<4x4xf32> {
+        %0 = chlo.broadcast_add %arg0, %arg1 : (tensor<1x4xf32>, tensor<4x1xf32>) -> tensor<4x4xf32>
+        %1 = "mhlo.abs"(%0) : (tensor<4x4xf32>) -> tensor<4x4xf32>
+        return %1 : tensor<4x4xf32>
+      }
+}"""
+
+arg0 = np.ones((1, 4)).astype(np.float32)
+arg1 = np.ones((4, 1)).astype(np.float32)
+
+shark_module = SharkInference(mhlo_ir, (arg0, arg1))
+shark_module.set_frontend("mhlo")
+shark_module.compile()
+print(shark_module.forward((arg0, arg1)))
+```
+
+
 
 ### Model Tracking (Shark Inference)