You will be shown an excerpt from a computer science scientific research paper, followed by a question about the paper. Please answer the question. Do not include any text in your response other than the answer.

Context: Selecting the hardest negatives can in practice lead to bad local minima early on intraining, specifically it can result in a collapsed model (i.e. f(x)=0). Inorder to mitigate this, it helps to select x_{i}^{n} such that\left\|f(x_{i}^{a})-f(x_{i}^{p})\right\|_{2}^{2}<\left\|f(x_{i}^{a})-f(x_{i}^{n})\right\|_{2}^{2}\;.(4)We call these negative exemplars semi-hard, as they are further awayfrom the anchor than the positive exemplar, but still hard because the squareddistance is close to the anchor-positive distance. Those negatives lie insidethe margin \alpha.

Question: Could the model just choose the embedding function to be zero just to overcome the very small distance that might occur in case of choosing the hardest negatives during triplet selection?
No the zero embeddings can result in a collapsed model.