[D] What's the purpose of the transpose in official LoRA implementation code?

Just skimmed their official implementation code and curious about this.
For example, in their Embedding module they declared and used lora parameters like this:

self.lora_A = nn.Parameter(self.weight.new_zeros((r, num_embeddings)))
self.lora_B = nn.Parameter(self.weight.new_zeros((embedding_dim, r)))
...
self.weight.data -= (self.lora_B @ self.lora_A).transpose(0, 1) * self.scaling
...
after_A = F.embedding(
x, self.lora_A.transpose(0, 1), self.padding_idx, self.max_norm,
self.norm_type, self.scale_grad_by_freq, self.sparse
)
result += (after_A @ self.lora_B.transpose(0, 1)) * self.scaling
...

So, why don't they just declare like this and use without transpose?

self.lora_A = nn.Parameter(self.weight.new_zeros((r, embedding_dim)))
self.lora_B …

code data embedding example implementation lora machinelearning parameters scaling