Add t5(https://huggingface.co/docs/transformers/model_doc/t5) model.

shark/examples/shark_inference/t5_tf.py

@@ -0,0 +1,38 @@
+from PIL import Image
+import requests
+
+from transformers import T5Tokenizer, TFT5Model
+import tensorflow as tf
+from shark.shark_inference import SharkInference
+
+# Create a set of inputs
+t5_inputs = [
+    tf.TensorSpec(shape=[1, 10], dtype=tf.int32),
+    tf.TensorSpec(shape=[1, 10], dtype=tf.int32),
+]
+
+class T5Module(tf.Module):
+
+    def __init__(self):
+        super(T5Module, self).__init__()
+        self.m = TFT5Model.from_pretrained("t5-small")
+        self.m.predict = lambda x,y: self.m(input_ids=x, decoder_input_ids=y)
+
+    @tf.function(input_signature=t5_inputs)
+    def forward(self, input_ids, decoder_input_ids):
+        return self.m.predict(input_ids, decoder_input_ids)
+
+
+if __name__ == "__main__":
+    # Prepping Data
+    tokenizer = T5Tokenizer.from_pretrained("t5-small")
+    text = "I love the distilled version of models."
+    inputs = tokenizer(
+        text, return_tensors="tf"
+    ).input_ids
+
+    shark_module = SharkInference(
+        T5Module(), (inputs, inputs))
+    shark_module.set_frontend("tensorflow")
+    shark_module.compile()
+    print(shark_module.forward((inputs,inputs)))