Update the README.md

Update the shark_importer and shark_inference APIs.

README.md

@@ -139,18 +139,25 @@ pytest benchmarks
 ### Shark Inference API
 
 ```
-from shark_runner import SharkInference
 
-shark_module = SharkInference(
-        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
+from shark.shark_importer import SharkImporter
+
+# SharkImporter imports mlir file from the torch, tensorflow or tf-lite module.
+
+mlir_importer = SharkImporter(
+    torch_module,
+    (input),
+    frontend="torch",  #tf, #tf-lite
+)
+torch_mlir, func_name = mlir_importer.import_mlir(tracing_required=True)
+
+# SharkInference accepts mlir in linalg, mhlo, and tosa dialect.
+
+from shark.shark_inference import SharkInference
+shark_module = SharkInference(torch_mlir, func_name, device="cpu", mlir_dialect="linalg")
 shark_module.compile()
+result = shark_module.forward((input))
 
-result = shark_module.forward(inputs)
 ```
 
 
@@ -170,11 +177,9 @@ mhlo_ir = r"""builtin.module  {
 
 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 = SharkInference(mhlo_ir, func_name="forward", device="cpu", mlir_dialect="mhlo")
 shark_module.compile()
-print(shark_module.forward((arg0, arg1)))
+result = shark_module.forward((arg0, arg1))
 ```
 </details>