"padding_idx" will default to all zeros, but can be updated to
        another value to be used as the padding vector.
  * **max_norm** (*float**, **optional*) -- If given, each
    embedding vector with norm larger than "max_norm" is
    renormalized to have norm "max_norm".

  * **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" matrix will be a sparse tensor. See Notes for
    more details regarding sparse gradients.

Variables:
      weight (Tensor) -- the learnable weights of the module of
      shape (num_embeddings, embedding_dim) initialized from
      \mathcal{N}(0, 1)
Shape: