class_docstr: A word embedding wrapper module around `torch.nn.Embedding` with options to
  initialize the word embedding weights and add MLP layers acting on each word.
  
  Note: Embedding weights for UNK token are always initialized to zeros.
  
  Args:
      num_embeddings (int): Total number of words/tokens (vocabulary size).
      embedding_dim (int): Size of embedding vector.
      embeddings_weight (torch.Tensor): Pretrained weights to initialize the
          embedding table with.
      init_range (List[int]): Range of uniform distribution to initialize the
          weights with if `embeddings_weight` is None.
      unk_token_idx (int): Index of UNK token in the word vocabulary.
      mlp_layer_dims (List[int]): List of layer dimensions (if any) to add
          on top of the embedding lookup.
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code: def forward(self, input):
      return self.mlp(self.word_embedding(input))
  
file_path: pytext/pytext/models/embeddings/word_embedding.py
imports: import collections
  from typing import List, Optional
  import torch
  from pytext.config.field_config import WordFeatConfig
  from pytext.data.tensorizers import Tensorizer
  from pytext.fields import FieldMeta
  from pytext.models.embeddings.embedding_base import EmbeddingBase
  from pytext.utils.usage import log_class_usage
  from pytorch.text.fb.utils.embeddings import PretrainedEmbedding
  from torch import nn
  from torch.utils.tensorboard import SummaryWriter
label_desc: Use image node_id 4 for calling the WordEmbedding obj's underlying member method code with example usage: obj.forward(input) and returns: self