From 4f0a35a2faa4ddd786c383250c42d623b4988edf Mon Sep 17 00:00:00 2001
From: Vincent Moens <vmoens@meta.com>
Date: Fri, 21 Jun 2024 08:08:24 +0100
Subject: [PATCH] amend

---
 torchrl/modules/distributions/continuous.py       | 10 +++++-----
 torchrl/modules/distributions/discrete.py         |  2 +-
 torchrl/modules/distributions/truncated_normal.py |  2 +-
 3 files changed, 7 insertions(+), 7 deletions(-)

diff --git a/torchrl/modules/distributions/continuous.py b/torchrl/modules/distributions/continuous.py
index 2ea9cc3ee29..75dab5264c1 100644
--- a/torchrl/modules/distributions/continuous.py
+++ b/torchrl/modules/distributions/continuous.py
@@ -91,7 +91,7 @@ def mode(self):
         return self.base_dist.mean
 
     @property
-    def deterministic(self):
+    def deterministic_sample(self):
         return self.mean
 
 
@@ -294,7 +294,7 @@ def mode(self):
         return torch.max(torch.stack([m, a], -1), dim=-1)[0]
 
     @property
-    def deterministic(self):
+    def deterministic_sample(self):
         return self.mean
 
     def log_prob(self, value, **kwargs):
@@ -488,7 +488,7 @@ def mode(self):
         return self.deterministic
 
     @property
-    def deterministic(self):
+    def deterministic_sample(self):
         m = self.root_dist.mean
         for t in self.transforms:
             m = t(m)
@@ -619,7 +619,7 @@ def mode(self) -> torch.Tensor:
         return self.param
 
     @property
-    def deterministic(self):
+    def deterministic_sample(self):
         return self.mean
 
     @property
@@ -748,7 +748,7 @@ def mode(self) -> torch.Tensor:
         return mode
 
     @property
-    def deterministic(self):
+    def deterministic_sample(self):
         return self.mode
 
     @property
diff --git a/torchrl/modules/distributions/discrete.py b/torchrl/modules/distributions/discrete.py
index bfed6d29bec..c48d8168887 100644
--- a/torchrl/modules/distributions/discrete.py
+++ b/torchrl/modules/distributions/discrete.py
@@ -107,7 +107,7 @@ def mode(self) -> torch.Tensor:
             return (self.probs == self.probs.max(-1, True)[0]).to(torch.long)
 
     @property
-    def deterministic(self):
+    def deterministic_sample(self):
         return self.mode
 
     @_one_hot_wrapper(D.Categorical)
diff --git a/torchrl/modules/distributions/truncated_normal.py b/torchrl/modules/distributions/truncated_normal.py
index cf8610ceb57..1350aeb2bc3 100644
--- a/torchrl/modules/distributions/truncated_normal.py
+++ b/torchrl/modules/distributions/truncated_normal.py
@@ -86,7 +86,7 @@ def mean(self):
         return self._mean
 
     @property
-    def deterministic(self):
+    def deterministic_sample(self):
         return self.mean
 
     @property