class_docstr: An MLP embedding wrapper module around `torch.nn.Embedding` to add
  transformations for float tensors.
  
  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.
      mlp_layer_dims (List[int]): List of layer dimensions (if any) to add
          on top of the embedding lookup.
class_docstr_tok: ["An","MLP","embedding","wrapper","module","around","`","torch.nn.Embedding","`","to","add","transformations","for","float","tensors",".","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",".","mlp_layer_dims","(","List","[","int","]",")",":","List","of","layer","dimensions","(","if","any",")","to","add","on","top","of","the","embedding","lookup","."]
code: def forward(self, input):
      return self.mlp(input)
  
file_path: pytext/pytext/models/embeddings/mlp_embedding.py
imports: from typing import List, Optional
  import torch
  from pytext.config.field_config import MLPFeatConfig
  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 torch import nn
  from torch.utils.tensorboard import SummaryWriter
label_desc: Use image node_id 3 for calling the MLPEmbedding obj's underlying member method code with example usage: obj.forward(input) and returns: self