docs: Fixed variables in f-strings (#20087)

* docs: Fixed variables in f-strings

* Replace unknown `block` with known `block_type` in ValueError

Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* Add missing torch import in docs code block

Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

docs/source/en/custom_models.mdx

@@ -55,9 +55,9 @@ class ResnetConfig(PretrainedConfig):
         **kwargs,
     ):
         if block_type not in ["basic", "bottleneck"]:
-            raise ValueError(f"`block` must be 'basic' or bottleneck', got {block}.")
+            raise ValueError(f"`block_type` must be 'basic' or bottleneck', got {block_type}.")
         if stem_type not in ["", "deep", "deep-tiered"]:
-            raise ValueError(f"`stem_type` must be '', 'deep' or 'deep-tiered', got {block}.")
+            raise ValueError(f"`stem_type` must be '', 'deep' or 'deep-tiered', got {stem_type}.")
 
         self.block_type = block_type
         self.layers = layers
@@ -146,6 +146,9 @@ class ResnetModel(PreTrainedModel):
 For the model that will classify images, we just change the forward method:
 
 ```py
+import torch
+
+
 class ResnetModelForImageClassification(PreTrainedModel):
     config_class = ResnetConfig
 

docs/source/es/custom_models.mdx

@@ -55,9 +55,9 @@ class ResnetConfig(PretrainedConfig):
         **kwargs,
     ):
         if block_type not in ["basic", "bottleneck"]:
-            raise ValueError(f"`block` must be 'basic' or bottleneck', got {block}.")
+            raise ValueError(f"`block_type` must be 'basic' or bottleneck', got {block_type}.")
         if stem_type not in ["", "deep", "deep-tiered"]:
-            raise ValueError(f"`stem_type` must be '', 'deep' or 'deep-tiered', got {block}.")
+            raise ValueError(f"`stem_type` must be '', 'deep' or 'deep-tiered', got {stem_type}.")
 
         self.block_type = block_type
         self.layers = layers
@@ -145,6 +145,9 @@ class ResnetModel(PreTrainedModel):
 Para el modelo que clasificará las imágenes, solo cambiamos el método de avance (es decir, el método `forward`):
 
 ```py
+import torch
+
+
 class ResnetModelForImageClassification(PreTrainedModel):
     config_class = ResnetConfig
 

docs/source/it/custom_models.mdx

@@ -54,9 +54,9 @@ class ResnetConfig(PretrainedConfig):
         **kwargs,
     ):
         if block_type not in ["basic", "bottleneck"]:
-            raise ValueError(f"`block` must be 'basic' or bottleneck', got {block}.")
+            raise ValueError(f"`block_type` must be 'basic' or bottleneck', got {block_type}.")
         if stem_type not in ["", "deep", "deep-tiered"]:
-            raise ValueError(f"`stem_type` must be '', 'deep' or 'deep-tiered', got {block}.")
+            raise ValueError(f"`stem_type` must be '', 'deep' or 'deep-tiered', got {stem_type}.")
 
         self.block_type = block_type
         self.layers = layers
@@ -146,6 +146,9 @@ class ResnetModel(PreTrainedModel):
 Per il modello che classificherà le immagini, cambiamo soltanto il metodo forward:
 
 ```py
+import torch
+
+
 class ResnetModelForImageClassification(PreTrainedModel):
     config_class = ResnetConfig
 

docs/source/pt/custom_models.mdx

@@ -54,9 +54,9 @@ class ResnetConfig(PretrainedConfig):
         **kwargs,
     ):
         if block_type not in ["basic", "bottleneck"]:
-            raise ValueError(f"`block` must be 'basic' or bottleneck', got {block}.")
+            raise ValueError(f"`block_type` must be 'basic' or bottleneck', got {block_type}.")
         if stem_type not in ["", "deep", "deep-tiered"]:
-            raise ValueError(f"`stem_type` must be '', 'deep' or 'deep-tiered', got {block}.")
+            raise ValueError(f"`stem_type` must be '', 'deep' or 'deep-tiered', got {stem_type}.")
 
         self.block_type = block_type
         self.layers = layers
@@ -145,6 +145,9 @@ class ResnetModel(PreTrainedModel):
 Para o modelo que irá classificar as imagens, vamos apenas alterar o método forward:
 
 ```py
+import torch
+
+
 class ResnetModelForImageClassification(PreTrainedModel):
     config_class = ResnetConfig