Adapted the examples to the small code changes. Replace add_ with add in...

Adapted the examples to the small code changes. Replace add_ with add in WideDeep to avoid annoying warnings. Replace output_dim with pred_dim in Wide for consistentcy

pytorch_widedeep/models/wide.py

@@ -5,14 +5,14 @@ from ..wdtypes import *
 
 class Wide(nn.Module):
     r"""Simple linear layer that will receive the one-hot encoded `'wide'`
-    input and connect it to the output neuron.
+    input and connect it to the output neuron(s).
 
     Parameters
     -----------
     wide_dim: int
         size of the input tensor
-    output_dim: int
-        size of the ouput tensor
+    pred_dim: int
+        size of the ouput tensor containing the predictions
 
     Attributes
     -----------
@@ -24,7 +24,7 @@ class Wide(nn.Module):
     >>> import torch
     >>> from pytorch_widedeep.models import Wide
     >>> X = torch.empty(4, 4).random_(2)
-    >>> wide = Wide(wide_dim=X.size(0), output_dim=1)
+    >>> wide = Wide(wide_dim=X.size(0), pred_dim=1)
     >>> wide(X)
     tensor([[-0.8841],
             [-0.8633],
@@ -32,9 +32,9 @@ class Wide(nn.Module):
             [-0.4762]], grad_fn=<AddmmBackward>)
     """
 
-    def __init__(self, wide_dim: int, output_dim: int = 1):
+    def __init__(self, wide_dim: int, pred_dim: int = 1):
         super(Wide, self).__init__()
-        self.wide_linear = nn.Linear(wide_dim, output_dim)
+        self.wide_linear = nn.Linear(wide_dim, pred_dim)
 
     def forward(self, X: Tensor) -> Tensor:  # type: ignore
         r"""Forward pass. Simply connecting the one-hot encoded input with the

pytorch_widedeep/models/wide_deep.py

@@ -201,13 +201,13 @@ class WideDeep(nn.Module):
             if self.deepimage is not None:
                 deepside = torch.cat([deepside, self.deepimage(X["deepimage"])], axis=1)  # type: ignore
             deepside_out = self.deephead(deepside)
-            return out.add_(deepside_out)
+            return out.add(deepside_out)
         else:
-            out.add_(self.deepdense(X["deepdense"]))
+            out.add(self.deepdense(X["deepdense"]))
             if self.deeptext is not None:
-                out.add_(self.deeptext(X["deeptext"]))
+                out.add(self.deeptext(X["deeptext"]))
             if self.deepimage is not None:
-                out.add_(self.deepimage(X["deepimage"]))
+                out.add(self.deepimage(X["deepimage"]))
             return out
 
     def compile(