Added distribution method nodes that take 2d tensor as input

modules/pytorch_wrapper/ptdm_log_prob.py

@@ -25,7 +25,7 @@ class PtdmLogProb:
         return {
             "required": {
                 "distribution": ("PTDISTRIBUTION", {}),
-                "value": ("STRING", {"default": ""})
+                "x": ("STRING", {"default": ""})
             }
         }
 
@@ -35,27 +35,27 @@ class PtdmLogProb:
 
     def f(self,
           distribution:torch.distributions.distribution.Distribution,
-          value: str) -> tuple:
+          x: str) -> tuple:
         """
         Computes the probability for the input distribution.
 
         Args:
             distribution (torch.distributions.distribution.Distribution): Distribution.
-            value (Union[int,float])): Value or number of successes.
+            x (Union[int,float])): Value or number of successes.
 
         Returns:
             tuple containing the log of probability
         """
 
-        k = ast.literal_eval(value)
-        if isinstance(k, int) is False and isinstance(k, float) is False :
+        v = ast.literal_eval(x)
+        if isinstance(v, int) is False and isinstance(v, float) is False :
             if isinstance(distribution, Dirichlet) is False:
                 raise ValueError("You need to specify a float or an int.")
 
         if isinstance(distribution, (Poisson, Categorical, Multinomial, Binomial, NegativeBinomial)):
-            tens = torch.tensor(k, dtype=torch.int64)
+            tens = torch.tensor(v, dtype=torch.int64)
         else:
-            tens = torch.tensor(k, dtype=torch.float32)
+            tens = torch.tensor(v, dtype=torch.float32)
 
         return (distribution.log_prob(tens),)