A detailed schematic diagram of the Transformer neural network architecture, inspired by the original Vaswani et al. (2017) paper. The diagram should include key components such as multi-head self-attention, positional encoding, feed-forward layers, layer normalization, and residual connections. The architecture should be divided into an encoder and decoder, each with multiple stacked layers. The encoder should process input embeddings through self-attention and feed-forward layers, while the decoder should include masked self-attention, cross-attention, and feed-forward layers. Arrows should indicate the flow of data between layers, and softmax should be shown at the output. The style should be clean and professional, with distinct colors representing different components.
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