alse``. Default: ``True``
            padding_idx (int, optional): If specified, the entries at :attr:`padding_idx` do not contribute to the gradient;
                                         therefore, the embedding vector at :attr:`padding_idx` is not updated during training,
                                         i.e. it remains as a fixed "pad".
            max_norm (float, optional): See module initialization documentation.
            norm_type (float, optional): See module initialization documentation. Default ``2``.
            scale_grad_by_freq (bool, optional): See module initialization documentation. Default ``False``.
            sparse (bool, optional): See module initialization documentation.

        Examples::

            >>> # FloatTensor containing pretrained weights
            >>> weight = torch.FloatTensor([[1, 2.3, 3], [4, 5.1, 6.3]])
            >>> embedding = nn.Embedding.from_pretrained(weight)
            >>> # Get embeddings for index 1
            >>> input = torch.LongTensor([1])
            >>> # xdoctest: +IGNORE_WANT("non-deterministic")
            >>> embedding(input)
            tensor([[ 4.0000,  5.1000,  6.3000]])
        r