From 392607a947b0a5d7909531509df11c86a8476815 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?=E5=AE=A6=E6=99=93=E7=8E=B2?= <3174348550@qq.com>
Date: Mon, 29 Sep 2025 14:48:13 +0800
Subject: [PATCH] modify code contents

---
 docs/lite/docs/source_en/train/runtime_train_cpp.md    | 10 +++++-----
 docs/lite/docs/source_zh_cn/train/runtime_train_cpp.md | 10 +++++-----
 2 files changed, 10 insertions(+), 10 deletions(-)

diff --git a/docs/lite/docs/source_en/train/runtime_train_cpp.md b/docs/lite/docs/source_en/train/runtime_train_cpp.md
index d10fc75f5b..95a7a8d21e 100644
--- a/docs/lite/docs/source_en/train/runtime_train_cpp.md
+++ b/docs/lite/docs/source_en/train/runtime_train_cpp.md
@@ -259,7 +259,7 @@ MindSpore Lite provides the following methods to obtain model input tensors:
     /// \brief Obtains the number of elements of the MSTensor.
     ///
     /// \return The number of elements of the MSTensor.
-    int64_t ElementNum() const;
+    int64_t ElementsNum() const;
 
     /// \brief Obtains the data type of the MSTensor.
     ///
@@ -291,7 +291,7 @@ MindSpore Lite provides the following methods to obtain model input tensors:
     // Assuming batch_size and data_size variables hold the Batch size and the size of a single data tensor, respectively:
     // And assuming sparse labels are used
     if ((inputs.at(data_index)->Size() != batch_size*data_size) ||
-        (inputs.at(label_index)->ElementNum() != batch_size)) {
+        (inputs.at(label_index)->ElementsNum() != batch_size)) {
         std::cerr << "Input data size does not match model input" << std::endl;
         return -1;
     }
@@ -381,7 +381,7 @@ MindSpore Lite provides the following methods to obtain the model's output `MSTe
     /// \brief Obtains the number of elements of the MSTensor.
     ///
     /// \return The number of elements of the MSTensor.
-    int64_t ElementNum() const;
+    int64_t ElementsNum() const;
 
     /// \brief Obtains the data type of the MSTensor.
     ///
@@ -401,7 +401,7 @@ MindSpore Lite provides the following methods to obtain the model's output `MSTe
     auto out_tensors = model->GetOutputs();
     for (auto out_tensor : out_tensors) {
       std::cout << "tensor name is:" << out_tensor.Name() << " tensor size is:" << out_tensor.DataSize()
-                << " tensor elements num is:" << out_tensor.ElementNum() << std::endl;
+                << " tensor elements num is:" << out_tensor.ElementsNum() << std::endl;
       // The model output data is float 32.
       if (out_tensor.DataType() != mindspore::DataType::kNumberTypeFloat32) {
         std::cerr << "Output should in float32" << std::endl;
@@ -413,7 +413,7 @@ MindSpore Lite provides the following methods to obtain the model's output `MSTe
         return -1;
       }
       std::cout << "output data is:";
-      for (int i = 0; i < out_tensor.ElementNum() && i < 10; i++) {
+      for (int i = 0; i < out_tensor.ElementsNum() && i < 10; i++) {
         std::cout << out_data[i] << " ";
       }
       std::cout << std::endl;
diff --git a/docs/lite/docs/source_zh_cn/train/runtime_train_cpp.md b/docs/lite/docs/source_zh_cn/train/runtime_train_cpp.md
index 62928fd10d..c2424bb6f5 100644
--- a/docs/lite/docs/source_zh_cn/train/runtime_train_cpp.md
+++ b/docs/lite/docs/source_zh_cn/train/runtime_train_cpp.md
@@ -257,7 +257,7 @@ return model->Resize(inputs, new_shapes);
     /// \brief Obtains the number of elements of the MSTensor.
     ///
     /// \return The number of elements of the MSTensor.
-    int64_t ElementNum() const;
+    int64_t ElementsNum() const;
 
     /// \brief Obtains the data type of the MSTensor.
     ///
@@ -289,7 +289,7 @@ return model->Resize(inputs, new_shapes);
     // Assuming batch_size and data_size variables hold the Batch size and the size of a single data tensor, respectively:
     // And assuming sparse labels are used
     if ((inputs.at(data_index)->Size() != batch_size*data_size) ||
-        (inputs.at(label_index)->ElementNum() != batch_size)) {
+        (inputs.at(label_index)->ElementsNum() != batch_size)) {
         std::cerr << "Input data size does not match model input" << std::endl;
         return -1;
     }
@@ -379,7 +379,7 @@ MindSpore Lite提供下列方法来获取模型的输出张量：
     /// \brief Obtains the number of elements of the MSTensor.
     ///
     /// \return The number of elements of the MSTensor.
-    int64_t ElementNum() const;
+    int64_t ElementsNum() const;
 
     /// \brief Obtains the data type of the MSTensor.
     ///
@@ -399,7 +399,7 @@ MindSpore Lite提供下列方法来获取模型的输出张量：
     auto out_tensors = model->GetOutputs();
     for (auto out_tensor : out_tensors) {
       std::cout << "tensor name is:" << out_tensor.Name() << " tensor size is:" << out_tensor.DataSize()
-                << " tensor elements num is:" << out_tensor.ElementNum() << std::endl;
+                << " tensor elements num is:" << out_tensor.ElementsNum() << std::endl;
       // The model output data is float 32.
       if (out_tensor.DataType() != mindspore::DataType::kNumberTypeFloat32) {
         std::cerr << "Output should in float32" << std::endl;
@@ -411,7 +411,7 @@ MindSpore Lite提供下列方法来获取模型的输出张量：
         return -1;
       }
       std::cout << "output data is:";
-      for (int i = 0; i < out_tensor.ElementNum() && i < 10; i++) {
+      for (int i = 0; i < out_tensor.ElementsNum() && i < 10; i++) {
         std::cout << out_data[i] << " ";
       }
       std::cout << std::endl;
-- 
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