therefore, the embedding vector at "padding_idx" is not
        updated during training, i.e. it remains as a fixed "pad".
  * **max_norm** (*float**, **optional*) -- If given, each
    embedding vector with norm larger than "max_norm" is
    renormalized to have norm "max_norm". Note: this will modify
    "weight" in-place.

  * **norm_type** (*float**, **optional*) -- The p of the p-norm
    to compute for the "max_norm" option. Default "2".

  * **scale_grad_by_freq** (*bool**, **optional*) -- If given,
    this will scale gradients by the inverse of frequency of the
    words in the mini-batch. Default "False".

  * **sparse** (*bool**, **optional*) -- If "True", gradient
    w.r.t. "weight" will be a sparse tensor. See Notes under
    "torch.nn.Embedding" for more details regarding sparse
    gradients.

Return type:
      Tensor
Shape:
      * Input: LongTensor of arbitrary shape containing the indices to
        extract