fix link to shape inference section

website/docs/api/dependencyparser.md

@@ -297,7 +297,8 @@ Add a new label to the pipe. Note that you don't have to call this method if you
 provide a **representative data sample** to the
 [`begin_training`](#begin_training) method. In this case, all labels found in
 the sample will be automatically added to the model, and the output dimension
-will be [inferred](/usage/layers-architectures#shape-inference) automatically.
+will be [inferred](/usage/layers-architectures#thinc-shape-inference)
+automatically.
 
 > #### Example
 >

website/docs/api/entityrecognizer.md

@@ -285,7 +285,8 @@ Add a new label to the pipe. Note that you don't have to call this method if you
 provide a **representative data sample** to the
 [`begin_training`](#begin_training) method. In this case, all labels found in
 the sample will be automatically added to the model, and the output dimension
-will be [inferred](/usage/layers-architectures#shape-inference) automatically.
+will be [inferred](/usage/layers-architectures#thinc-shape-inference)
+automatically.
 
 > #### Example
 >

website/docs/api/language.md

@@ -205,8 +205,8 @@ examples can either be the full training data or a representative sample. They
 are used to **initialize the models** of trainable pipeline components and are
 passed each component's [`begin_training`](/api/pipe#begin_training) method, if
 available. Initialization includes validating the network,
-[inferring missing shapes](/usage/layers-architectures#shape-inference) and
-setting up the label scheme based on the data.
+[inferring missing shapes](/usage/layers-architectures#thinc-shape-inference)
+and setting up the label scheme based on the data.
 
 If no `get_examples` function is provided when calling `nlp.begin_training`, the
 pipeline components will be initialized with generic data. In this case, it is

website/docs/api/morphologizer.md

@@ -263,7 +263,8 @@ already been fully [initialized](#begin_training). Note that you don't have to
 call this method if you provide a **representative data sample** to the
 [`begin_training`](#begin_training) method. In this case, all labels found in
 the sample will be automatically added to the model, and the output dimension
-will be [inferred](/usage/layers-architectures#shape-inference) automatically.
+will be [inferred](/usage/layers-architectures#thinc-shape-inference)
+automatically.
 
 > #### Example
 >

website/docs/api/pipe.md

@@ -317,7 +317,7 @@ Note that in general, you don't have to call `pipe.add_label` if you provide a
 representative data sample to the [`begin_training`](#begin_training) method. In
 this case, all labels found in the sample will be automatically added to the
 model, and the output dimension will be
-[inferred](/usage/layers-architectures#shape-inference) automatically.
+[inferred](/usage/layers-architectures#thinc-shape-inference) automatically.
 
 ## Pipe.is_resizable {#is_resizable tag="method"}
 

website/docs/api/tagger.md

@@ -293,8 +293,8 @@ set, or if the model has already been fully [initialized](#begin_training). Note
 that you don't have to call this method if you provide a **representative data
 sample** to the [`begin_training`](#begin_training) method. In this case, all
 labels found in the sample will be automatically added to the model, and the
-output dimension will be [inferred](/usage/layers-architectures#shape-inference)
-automatically.
+output dimension will be
+[inferred](/usage/layers-architectures#thinc-shape-inference) automatically.
 
 > #### Example
 >

website/docs/api/textcategorizer.md

@@ -302,8 +302,8 @@ set, or if the model has already been fully [initialized](#begin_training). Note
 that you don't have to call this method if you provide a **representative data
 sample** to the [`begin_training`](#begin_training) method. In this case, all
 labels found in the sample will be automatically added to the model, and the
-output dimension will be [inferred](/usage/layers-architectures#shape-inference)
-automatically.
+output dimension will be
+[inferred](/usage/layers-architectures#thinc-shape-inference) automatically.
 
 > #### Example
 >