Add comprehensive main workflow test for Python debugging with image viewing

[Copilot]

This PR implements a comprehensive test suite that validates the main workflow of the "Simply View Image for Python Debugging" extension. The test covers the complete user journey from setting up a Python debugging session with image data to viewing images through the extension's webview interface.

What was added

Main Workflow Python Test Script (python_test/main_workflow_test.py)

Created a comprehensive test script that simulates real-world usage scenarios:

• Creates various image and tensor data structures using built-in Python (no external dependencies)
• Simulates numpy-like arrays with nested lists for compatibility
• Provides multiple strategic breakpoint locations for testing
• Includes metadata for different data types (RGB images, grayscale, tensors)

Comprehensive UI Test (tests/ui-test/python-debug.test.ts)

Implemented a full end-to-end test that validates:

• Opening Python scripts with image/tensor variables
• Setting breakpoints on lines containing image data
• Starting Python debug sessions and verifying breakpoint hits
• Testing extension's image viewing commands during active debugging
• Validating webview functionality for image display
• Handling multiple data types and debugging scenarios

Enhanced Test Infrastructure

• Added run-basic-tests.mjs for CI-compatible validation tests
• Updated validation tests to match the actual implementation
• Enhanced launch configuration to support the main workflow test
• Updated documentation with comprehensive testing guidance

Test Coverage

The main workflow test validates the complete extension workflow:

describe('main Workflow: Python Debugging with Image Viewing', function () {
  // Tests the complete journey:
  // 1. Script preparation with image/tensor data
  // 2. Debugging setup with strategic breakpoint placement  
  // 3. Debug session initiation and breakpoint verification
  // 4. Extension integration testing during active debugging
  // 5. Webview functionality for image display
  // 6. Multiple data type support (RGB, grayscale, tensors)
});

The Python test script creates realistic debugging scenarios:

def main():
    # Basic debugging variable
    x = "hello"
    print(f"The value of x is: {x}")  # Breakpoint location 1
    
    # Image data structures for extension testing
    sample_image = create_sample_image()  # Breakpoint location 2
    sample_tensor = create_sample_tensor()  # Breakpoint location 3
    
    # Multiple data types for comprehensive testing
    grayscale_image = create_numpy_like_array([64, 64], 'uint8')
    float_image = create_numpy_like_array([32, 32, 3], 'float32')

Benefits

• Complete Workflow Coverage: Tests the extension's primary use case end-to-end
• CI Compatible: Designed to work in headless environments with proper error handling
• No External Dependencies: Uses built-in Python structures to avoid dependency issues
• Comprehensive Documentation: Updated README with detailed testing guidance
• Code Quality: All code passes linting and follows repository standards

This implementation ensures that the extension's main workflow - viewing images and tensors during Python debugging sessions - works correctly and can be validated automatically in CI environments.

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