Rename "network" to "model" in preprocessing API (#9054)

samples/cpp/classification_sample_async/main.cpp

@@ -110,7 +110,7 @@ int main(int argc, char* argv[]) {
         // - layout of data is 'NHWC'
         input_info.tensor().set_element_type(ov::element::u8).set_layout(tensor_layout);
         // 3) Here we suppose model has 'NCHW' layout for input
-        input_info.network().set_layout("NCHW");
+        input_info.model().set_layout("NCHW");
         // 4) output() with no args assumes a model has a single result
         // - output() with no args assumes a model has a single result
         // - precision of tensor is supposed to be 'f32'

samples/cpp/hello_classification/main.cpp

@@ -86,7 +86,7 @@ int tmain(int argc, tchar* argv[]) {
         // - apply linear resize from tensor spatial dims to model spatial dims
         preproc.input().preprocess().resize(ov::preprocess::ResizeAlgorithm::RESIZE_LINEAR);
         // 4) Here we suppose model has 'NCHW' layout for input
-        preproc.input().network().set_layout("NCHW");
+        preproc.input().model().set_layout("NCHW");
         // 5) Set output tensor information:
         // - precision of tensor is supposed to be 'f32'
         preproc.output().tensor().set_element_type(ov::element::f32);

samples/cpp/ngraph_function_creation_sample/main.cpp

@@ -284,7 +284,7 @@ int main(int argc, char* argv[]) {
         // - precision of tensor is supposed to be 'u8'
         input_info.tensor().set_layout(tensor_layout).set_element_type(element::u8);
         // 3) Here we suppose model has 'NCHW' layout for input
-        input_info.network().set_layout("NCHW");
+        input_info.model().set_layout("NCHW");
         // 4) Once the build() method is called, the preprocessing steps
         // for layout and precision conversions are inserted automatically
         model = proc.build();