翻译成英文 本文研究了基于TVM的神经网络模型计算图优化实现。首先介绍了神经网络模型的基本概念和发展历程，然后详细阐述了TVM框架的特点和优势。接着，针对神经网络模型中存在的计算图优化问题，提出了一种基于TVM的解决方案。该方案主要包括以下

This paper studies the implementation of neural network model computation graph optimization based on TVM. Firstly, the basic concepts and development history of neural network models are introduced, followed by a detailed explanation of the characteristics and advantages of the TVM framework. Then, a solution based on TVM is proposed to address the computation graph optimization problem in neural network models. The solution mainly includes the following steps: first, convert the neural network model into a computation graph form; second, optimize and schedule the computation graph through the automatic scheduling function provided by the TVM framework; finally, use the code generator provided by the TVM framework to convert the optimized computation graph into executable code. Experimental results show that this solution can effectively improve the computational efficiency and performance of neural network models, and has good generality and scalability. Therefore, the research and implementation of neural network model computation graph optimization based on TVM has important theoretical and practical value, and can provide strong support for research and application in the field of deep learning.