code: def from_config(cls, config, tensorizers):
      embedding = create_module(
          config.embedding, tensorizer=tensorizers["tokens"]
      )
      representation = create_module(
          config.representation, embed_dim=embedding.embedding_dim
      )
      slots = tensorizers["slots"].vocab
      decoder = create_module(
          config.decoder,
          in_dim=representation.representation_dim,
          out_dim=len(slots),
      )
      output_layer = MyTaggingOutputLayer(slots, CrossEntropyLoss(None))
      return cls(embedding, representation, decoder, output_layer)
  
file_path: pytext/demo/my_tagging/model.py
imports: from typing import List
  import torch
  from pytext.config.pytext_config import ConfigBase
  from pytext.data.tensorizers import TokenTensorizer
  from pytext.loss import CrossEntropyLoss
  from pytext.models.decoders.mlp_decoder import MLPDecoder
  from pytext.models.embeddings import WordEmbedding
  from pytext.models.model import Model
  from pytext.models.module import create_module
  from pytext.models.representations.bilstm_slot_attn import BiLSTMSlotAttention
  from .output import MyTaggingOutputLayer
label_desc: Use image node_id 1 for calling the MyTaggingModel obj's underlying member method code with example usage: obj.from_config(cls, config, tensorizers) and returns: cls