code: def compute_sims(inputs: mx.nd.NDArray, normalize: bool) -> mx.nd.NDArray:
      
      if normalize:
          logger.info("Normalizing embeddings to unit length")
          inputs = mx.nd.L2Normalization(inputs, mode='instance')
      sims = mx.nd.dot(inputs, inputs, transpose_b=True)
      sims_np = sims.asnumpy()
      np.fill_diagonal(sims_np, -9999999.)
      sims = mx.nd.array(sims_np)
      return sims
docs: Returns a matrix with pair-wise similarity scores between inputs.
      Similarity score is (normalized) Euclidean distance. 'Similarity with self' is masked
      to large negative value.
  
      :param inputs: NDArray of inputs.
      :param normalize: Whether to normalize to unit-length.
      :return: NDArray with pairwise similarities of same shape as inputs.