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The shape of the 'attention' tensor is torch.Size([32, 32]). The matrix multiplication of (32, 256) with (512, 32) results in a (32, 32) tensor. The crucial issue here is the batch-wise attention calculation. The attention weights are being computed between all text embeddings and all image embeddings in the batch. During training, this leads to 'information leakage' because the model is learning relationships between samples that shouldn't be related (i.e., different text-image pairs in the batch are influencing each other). For proper cross-modal attention, you would typically want to compute the attention weights only between corresponding text and image embeddings within the same sample.