class_docstr: Performs a common operation (embedding) that won't work on an empty tensor.
  Returns an arbitrary loss.
class_docstr_tok: ["Performs","a","common","operation","(","embedding",")","that","wo","n't","work","on","an","empty","tensor",".","Returns","an","arbitrary","loss","."]
code: def __init__(self, vocab: Vocabulary) -> None:
      super().__init__(vocab)
      weight = torch.ones(vocab.get_vocab_size(), 10)
      token_embedding = Embedding(
          num_embeddings=vocab.get_vocab_size(),
          embedding_dim=10,
          weight=weight,
          trainable=False,
      )
      self.embedder = BasicTextFieldEmbedder({"words": token_embedding})
  
imports: from typing import Dict
  import numpy
  import torch
  from allennlp.common.testing import AllenNlpTestCase
  from allennlp.data import Token, Vocabulary, Instance
  from allennlp.data.fields import TextField, LabelField, ListField, IndexField, SequenceLabelField
  from allennlp.data.token_indexers import SingleIdTokenIndexer, TokenCharactersIndexer
  from allennlp.data.data_loaders import SimpleDataLoader
  from allennlp.data.tokenizers import SpacyTokenizer
  from allennlp.models import Model
  from allennlp.modules import Embedding
  from allennlp.modules.text_field_embedders import BasicTextFieldEmbedder
label_desc: Use image node_id 1 to create a new DummyModel object from inherited base classes: Model with example: obj = DummyModel(vocab)