--- /content/alphafold_copy/alphafold/model/model.py	2022-06-21 02:56:28.903256585 +0000
+++ /content/alphafold/alphafold/model/model.py	2022-06-21 03:08:21.392362049 +0000
@@ -50,7 +50,8 @@
 
   def __init__(self,
                config: ml_collections.ConfigDict,
-               params: Optional[Mapping[str, Mapping[str, np.ndarray]]] = None):
+               params: Optional[Mapping[str, Mapping[str, np.ndarray]]] = None,
+               is_training = False):
     self.config = config
     self.params = params
 
@@ -58,7 +59,7 @@
       model = modules.AlphaFold(self.config.model)
       return model(
           batch,
-          is_training=False,
+          is_training=is_training,
           compute_loss=False,
           ensemble_representations=True)
 
@@ -117,7 +118,7 @@
     logging.info('Output shape was %s', shape)
     return shape
 
-  def predict(self, feat: features.FeatureDict) -> Mapping[str, Any]:
+  def predict(self, feat: features.FeatureDict, random_seed=0) -> Mapping[str, Any]:
     """Makes a prediction by inferencing the model on the provided features.
 
     Args:
@@ -128,14 +129,28 @@
       A dictionary of model outputs.
     """
     self.init_params(feat)
-    logging.info('Running predict with shape(feat) = %s',
-                 tree.map_structure(lambda x: x.shape, feat))
-    result = self.apply(self.params, jax.random.PRNGKey(0), feat)
-    # This block is to ensure benchmark timings are accurate. Some blocking is
-    # already happening when computing get_confidence_metrics, and this ensures
-    # all outputs are blocked on.
-    jax.tree_map(lambda x: x.block_until_ready(), result)
-    result.update(get_confidence_metrics(result))
-    logging.info('Output shape was %s',
-                 tree.map_structure(lambda x: x.shape, result))
-    return result
+    logging.info('Running predict with shape(feat) = %s', tree.map_structure(lambda x: x.shape, feat))
+                 
+    aatype = feat["aatype"]
+    num_iters = self.config.model.num_recycle + 1
+    num_ensemble = self.config.data.eval.num_ensemble
+    L = aatype.shape[1]
+    result = {"prev":{'prev_msa_first_row': np.zeros([L,256]),
+                      'prev_pair': np.zeros([L,L,128]),
+                      'prev_pos': np.zeros([L,37,3])}}
+        
+    r = 0
+    key = jax.random.PRNGKey(random_seed)
+    while r < num_iters:
+      s = r * num_ensemble
+      e = (r+1) * num_ensemble
+      sub_feat = jax.tree_map(lambda x:x[s:e], feat)      
+      sub_feat["prev"] = result["prev"]
+      result, _ = self.apply(self.params, key, sub_feat)
+      del sub_feat
+      confidences = get_confidence_metrics(result)
+      result.update(confidences)
+      r += 1
+
+    logging.info('Output shape was %s', tree.map_structure(lambda x: x.shape, result))
+    return result, (r-1,0)