class_docstr: A more featureful embedding module than the default in Pytorch.  Adds the ability to:
  
      1. embed higher-order inputs
      2. pre-specify the weight matrix
      3. use a non-trainable embedding
      4. project the resultant embeddings to some other dimension (which only makes sense with
         non-trainable embeddings).
  
  Note that if you are using our data API and are trying to embed a
  [`TextField`](../../data/fields/text_field.md), you should use a
  [`TextFieldEmbedder`](../text_field_embedders/text_field_embedder.md) instead of using this directly.
  
  Registered as a `TokenEmbedder` with name "embedding".
  
  # Parameters
  
  num_embeddings : `int`
      Size of the dictionary of embeddings (vocabulary size).
  embedding_dim : `int`
      The size of each embedding vector.
  projection_dim : `int`, optional (default=`None`)
      If given, we add a projection layer after the embedding layer.  This really only makes
      sense if `trainable` is `False`.
  weight : `torch.FloatTensor`, optional (default=`None`)
      A pre-initialised weight matrix for the embedding lookup, allowing the use of
      pretrained vectors.
  padding_index : `int`, optional (default=`None`)
      If given, pads the output with zeros whenever it encounters the index.
  trainable : `bool`, optional (default=`True`)
      Whether or not to optimize the embedding parameters.
  max_norm : `float`, optional (default=`None`)
      If given, will renormalize the embeddings to always have a norm lesser than this
  norm_type : `float`, optional (default=`2`)
      The p of the p-norm to compute for the max_norm option
  scale_grad_by_freq : `bool`, optional (default=`False`)
      If given, this will scale gradients by the frequency of the words in the mini-batch.
  sparse : `bool`, optional (default=`False`)
      Whether or not the Pytorch backend should use a sparse representation of the embedding weight.
  vocab_namespace : `str`, optional (default=`None`)
      In case of fine-tuning/transfer learning, the model's embedding matrix needs to be
      extended according to the size of extended-vocabulary. To be able to know how much to
      extend the embedding-matrix, it's necessary to know which vocab_namspace was used to
      construct it in the original training. We store vocab_namespace used during the original
      training as an attribute, so that it can be retrieved during fine-tuning.
  pretrained_file : `str`, optional (default=`None`)
      Path to a file of word vectors to initialize the embedding matrix. It can be the
      path to a local file or a URL of a (cached) remote file. Two formats are supported:
          * hdf5 file - containing an embedding matrix in the form of a torch.Tensor;
          * text file - an utf-8 encoded text file with space separated fields.
  vocab : `Vocabulary`, optional (default = `None`)
      Used to construct an embedding from a pretrained file.
  
      In a typical AllenNLP configuration file, this parameter does not get an entry under the
      "embedding", it gets specified as a top-level parameter, then is passed in to this module
      separately.
  
  # Returns
  
  An Embedding module.
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code: def get_output_dim(self) -> int:
      return self.output_dim
  
imports: import io
  import itertools
  import logging
  import re
  import tarfile
  import warnings
  import zipfile
  from typing import Any, cast, Iterator, NamedTuple, Optional, Sequence, Tuple, BinaryIO
  import numpy
  import torch
  from torch.nn.functional import embedding
  from allennlp.common import Tqdm
  from allennlp.common.checks import ConfigurationError
  from allennlp.common.file_utils import cached_path, get_file_extension, is_url_or_existing_file
  from allennlp.data.vocabulary import Vocabulary
  from allennlp.modules.time_distributed import TimeDistributed
  from allennlp.modules.token_embedders.token_embedder import TokenEmbedder
  from allennlp.nn import util
label_desc: Use image node_id 2 for calling the Embedding obj's underlying member method code with example usage: obj.get_output_dim() and returns: self