Address PR feedback: Add validation, refactor code duplication, enhance tests

- Add regex pattern validation for execution_image parameter
- Refactor get_code_execution_tools() to eliminate code duplication using tool_kwargs
- Add comprehensive tests for invalid Docker image format validation
- Add tests for various valid Docker image formats
- Update documentation with best practices section for custom Docker images
- Fix lint issues: remove unused imports and variables

Addresses feedback from joaomdmoura in PR #2934

Co-Authored-By: João <joao@crewai.com>

docs/tools/ai-ml/codeinterpretertool.mdx

@@ -108,6 +108,30 @@ programmer_agent = Agent(
 )
 ```
 
+### Best Practices for Custom Docker Images
+
+When using custom Docker images for code execution, consider the following best practices:
+
+- **Use specific version tags**: Instead of `latest`, use specific version tags like `python:3.11-slim` for reproducible builds
+- **Security scanning**: Ensure your custom images are regularly scanned for security vulnerabilities
+- **Image size optimization**: Consider using slim or alpine variants to reduce image size and improve performance
+- **Pre-installed dependencies**: Include commonly used libraries in your custom image to avoid repeated installations
+- **Registry accessibility**: Ensure the Docker registry hosting your custom image is accessible from your execution environment
+
+Example with a custom image containing data science libraries:
+
+```python Code
+# Custom image with pre-installed data science packages
+data_scientist_agent = Agent(
+    role="Data Scientist",
+    goal="Analyze datasets and create visualizations",
+    backstory="Expert in data analysis with access to specialized tools",
+    allow_code_execution=True,
+    execution_image="my-registry.com/datascience:python3.11-pandas-numpy",
+    verbose=True,
+)
+```
+
 ### Enabling `unsafe_mode`
 
 ```python Code