In terms of speed, the "torch.bmm" function is generally faster than the "einsum" function in PyTorch. The "torch.bmm" function is specifically optimized for performing batch matrix multiplications, making it faster for this type of operation compared to the more general "einsum" function.

However, it's important to note that the difference in speed between these two functions can depend on the specific use case and the size of the input tensors. In some cases, the "einsum" function can be more efficient than "torch.bmm" if the operation can be expressed more efficiently using the Einstein summation notation.

In general, when optimizing a neural network implementation in PyTorch, it's important to consider the entire system, including the choice of optimizer, batch size, and data preprocessing, in addition to the choice of function for individual operations. The most efficient implementation will depend on the specific requirements and constraints of the task and system.
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