code: def DummyTransformerModel(width: int, depth: int):
      def _forward(model, tokens, is_train):
          width = model.attrs["width"]
          depth = model.attrs["depth"]
          shape = (
              depth,
              tokens.input_ids.shape[0],
              tokens.input_ids.shape[1],
              width,
          )
          tensors = torch.zeros(*shape)
          return (
              ModelOutput(last_hidden_state=tensors),
              lambda d_tensors: tokens,
          )
  
      return Model(
          "dummy-transformer",
          _forward,
          attrs={"width": width, "depth": depth},
      )
  
imports: from typing import Dict, List, Union
  import numpy
  import torch
  import copy
  from transformers.file_utils import ModelOutput
  from numpy.testing import assert_array_equal
  from spacy.tokens import Doc
  from thinc.api import Model, get_current_ops
  from ..data_classes import FullTransformerBatch, HFObjects
  from ..span_getters import get_doc_spans
  from ..layers.transformer_model import forward
label_desc: Use image node_id 2 for calling a global function with example usage: DummyTransformerModel(width, depth) and returns: Model, ModelOutput,  function