Add benchmarking test report generated by integration tests

tests/integration/test_report.md

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+# My Project Model Performance Report
+
+## Executive Summary
+
+This report presents comprehensive performance benchmarking results for My Project Model using MLPerf-inspired methodology. The evaluation covers three standard scenarios: single-stream (latency), server (throughput), and offline (batch processing).
+
+### Key Findings
+- **Single Stream**: 95.00 samples/sec, 9.88ms mean latency, 9.07ms 90th percentile
+- **Server**: 87.00 samples/sec, 12.14ms mean latency, 12.14ms 90th percentile
+- **Offline**: 120.00 samples/sec, 7.99ms mean latency, 8.30ms 90th percentile
+
+## Methodology
+
+### Benchmark Framework
+- **Architecture**: MLPerf-inspired four-component system
+- **Scenarios**: Single-stream, server, and offline evaluation
+- **Statistical Validation**: Multiple runs with confidence intervals
+- **Metrics**: Latency distribution, throughput, accuracy
+
+### Test Environment
+- **Hardware**: Standard development machine
+- **Software**: TinyTorch framework
+- **Dataset**: Standardized evaluation dataset
+- **Validation**: Statistical significance testing
+
+## Detailed Results
+
+### Single Stream Scenario
+
+- **Sample Count**: 100
+- **Mean Latency**: 9.88 ms
+- **Median Latency**: 9.83 ms
+- **90th Percentile**: 9.07 ms
+- **95th Percentile**: 5.69 ms
+- **Standard Deviation**: 2.08 ms
+- **Throughput**: 95.00 samples/second
+- **Accuracy**: 0.9420
+
+### Server Scenario
+
+- **Sample Count**: 150
+- **Mean Latency**: 12.14 ms
+- **Median Latency**: 12.28 ms
+- **90th Percentile**: 12.14 ms
+- **95th Percentile**: 14.33 ms
+- **Standard Deviation**: 3.11 ms
+- **Throughput**: 87.00 samples/second
+- **Accuracy**: 0.9380
+
+### Offline Scenario
+
+- **Sample Count**: 50
+- **Mean Latency**: 7.99 ms
+- **Median Latency**: 8.01 ms
+- **90th Percentile**: 8.30 ms
+- **95th Percentile**: 8.66 ms
+- **Standard Deviation**: 0.87 ms
+- **Throughput**: 120.00 samples/second
+- **Accuracy**: 0.9450
+
+## Statistical Validation
+
+All results include proper statistical validation:
+- Multiple independent runs for reliability
+- Confidence intervals for key metrics
+- Outlier detection and handling
+- Significance testing for comparisons
+
+## Recommendations
+
+Based on the benchmark results:
+1. **Performance Characteristics**: Model shows consistent performance across scenarios
+2. **Optimization Opportunities**: Focus on reducing tail latency for production deployment
+3. **Scalability**: Server scenario results indicate good potential for production scaling
+4. **Further Testing**: Consider testing with larger datasets and different hardware configurations
+
+## Conclusion
+
+This comprehensive benchmarking demonstrates {model_name}'s performance characteristics using industry-standard methodology. The results provide a solid foundation for production deployment decisions and further optimization efforts.