Docs referencing unspecified SEM behavior updated to reflect SEM infe…

…rence change

Summary: After making SEM optional, tutorials and docs need to be updated to reflect that unspecified SEM return values are treated as unknown instead of 0.0

Reviewed By: adamobeng

Differential Revision: D16833117

fbshipit-source-id: 5e7caf1192a006f11a945e1822bfdfb26c4bdb44

tutorials/gpei_hartmann_developer.ipynb

@@ -152,7 +152,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "If there is only one metric in the experiment – the objective – then evaluation function can return a single tuple of mean and SEM, in which case Ax will assume that evaluation corresponds to the objective. It can also return only the mean as a float, in which case Ax will assume that SEM is 0.0. For more details on evaluation function, refer to the \"Trial Evaluation\" section in the docs."
+        "If there is only one metric in the experiment – the objective – then evaluation function can return a single tuple of mean and SEM, in which case Ax will assume that evaluation corresponds to the objective. It can also return only the mean as a float, in which case Ax will treat SEM as unknown and use a model that can infer it. For more details on evaluation function, refer to the \"Trial Evaluation\" section in the docs."
       ]
     },
     {

tutorials/gpei_hartmann_loop.ipynb

@@ -142,7 +142,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "If there is only one metric in the experiment – the objective – then evaluation function can return a single tuple of mean and SEM, in which case Ax will assume that evaluation corresponds to the objective. It can also return only the mean as a float, in which case Ax will assume that SEM is 0.0. For more details on evaluation function, refer to the \"Trial Evaluation\" section in the docs."
+        "If there is only one metric in the experiment – the objective – then evaluation function can return a single tuple of mean and SEM, in which case Ax will assume that evaluation corresponds to the objective. It can also return only the mean as a float, in which case Ax will treat SEM as unknown and use a model that can infer it. For more details on evaluation function, refer to the \"Trial Evaluation\" section in the docs."
       ]
     },
     {

tutorials/gpei_hartmann_service.ipynb

@@ -235,7 +235,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "Result of the evaluation should generally be a mapping of the format: `{metric_name -> (mean, SEM)}`. If there is only one metric in the experiment – the objective – then evaluation function can return a single tuple of mean and SEM, in which case Ax will assume that evaluation corresponds to the objective. It can also return only the mean as a float, in which case Ax will assume that SEM is 0.0. For more details on evaluation function, refer to the \"Trial Evaluation\" section in the docs."
+        "Result of the evaluation should generally be a mapping of the format: `{metric_name -> (mean, SEM)}`. If there is only one metric in the experiment – the objective – then evaluation function can return a single tuple of mean and SEM, in which case Ax will assume that evaluation corresponds to the objective. It can also return only the mean as a float, in which case Ax will treat SEM as unknown and use a model that can infer it. For more details on evaluation function, refer to the \"Trial Evaluation\" section in the docs."
       ]
     },
     {