add custom pipeline

pipeline.py

@@ -0,0 +1,21 @@
+from typing import Dict, List, Any
+import random
+
+class PreTrainedPipeline():
+    def __init__(self, path=""):
+        # IMPLEMENT_THIS
+        # Preload all the elements you are going to need at inference.
+        # For instance your model, processors, tokenizer that might be needed.
+        # This function is only called once, so do all the heavy processing I/O here"""
+        self.x = random.randint(0, 1000)
+
+    def __call__(self, inputs: str) -> List[float]:
+        """
+        Args:
+            inputs (:obj:`str`):
+                a string to get the features from.
+        Return:
+            A :obj:`list` of floats: The features computed by the model.
+        """
+        # IMPLEMENT_THIS
+        return self.x, inputs