Project: s41467-024-44930-y | Function: ECGnet (python)

system: You are an expert Python programmer implementing algorithms from scientific papers.
Complete the masked function based on the algorithm description.
Follow Python best practices and maintain consistent code style.
Return **only** the complete Python function code.

user: Complete this Python function:

**DESCRIPTION:** Main CHDdECG neural network architecture that integrates ECG waveform features, clinical features, and wavelet features for congenital heart disease detection

**KEY TERMS:** {
  "input_signal": "raw ECG-waveform data",
  "input_clinical": "human-concept features",
  "input_wavelet": "wavelet transformation features",
  "ResBlock": "residual blocks for feature extraction",
  "EncoderLayer": "transformer encoder",
  "TemporalAttention": "temporal attention mechanism",
  "TabNet": "tabular feature processing",
  "fusion_block": "multi-modal feature fusion"
}

**CONTEXT CODE:** #CHDdECG
# Note: Imports are commented out for masked version to avoid dependency issues
# from layers_Resblock import InputConv
# from layers_Resblock import ResBlock
# import tensorflow as tf
# from layers_Transformer import EncoderLayer
# from layers_TemporalAttention import TemporalAttention
# from layers_Tabnet import TabNet
# from layers_Downsampling import TabNet_downsampling
#define 形式定义model
# def ECGnet(num_classes,input_signal,input_clinical,input_wavelet):
def ECGnet(num_classes):
    # MASKED: Main CHDdECG neural network architecture that integrates ECG waveform features, clinical features, and wavelet features for congenital heart disease detection implementation
    raise NotImplementedError("This function is masked for testing purposes.")


Complete the function implementation replacing "raise NotImplementedError".