classmethod from_pretrained(embeddings, freeze=True, padding_idx=None, max_norm=None, norm_type=2.0, scale_grad_by_freq=False, sparse=False)
  Creates Embedding instance from given 2-dimensional FloatTensor.

  Parameters:
     * **embeddings** (*Tensor*) -- FloatTensor containing weights
       for the Embedding. First dimension is being passed to
       Embedding as "num_embeddings", second as "embedding_dim".

     * **freeze** (*bool**, **optional*) -- If "True", the tensor
       does not get updated in the learning process. Equivalent to
       "embedding.weight.requires_grad = False". Default: "True"

     * **padding_idx** (*int**, **optional*) -- If specified, the
       entries at "padding_idx" do not contribute to the gradient;
       therefore, the embedding vector at "padding_idx" is not
       updated during training, i.e. it remains as a fixed "pad".

     * **max_norm** (*float**, **optional*) -- See module
