class_docstr: Base class for token level embedding modules.
  
  Args:
      embedding_dim (int): Size of embedding vector.
  
  Attributes:
      num_emb_modules (int): Number of ways to embed a token.
      embedding_dim (int): Size of embedding vector.
code: def __init__(self, embedding_dim: int):
      super().__init__()
      # By default has 1 embedding which is itself, for EmbeddingList, this num
      # can be greater than 1
      self.num_emb_modules = 1
      self.embedding_dim = embedding_dim
      log_class_usage(__class__)
  
file_path: pytext/pytext/models/embeddings/embedding_base.py
imports: from typing import Dict, List
  import torch.nn
  from pytext.models.module import Module
  from pytext.utils.usage import log_class_usage
  from torch.utils.tensorboard import SummaryWriter
label_desc: Use image node_id 1 to create a new EmbeddingBase object from inherited base classes: Module with example: obj = EmbeddingBase(embedding_dim)