code: def DummyTransformer(
      depth: int = 2, width: int = 4, get_spans=get_doc_spans
  ) -> Model[List[Doc], FullTransformerBatch]:
      """Create a test model that produces a FullTransformerBatch object."""
      hf_model = HFObjects(DummyTokenizer(), None, None)
  
      return DummyModel(
          "dummy-transformer",
          transformer_forward,
          layers=[DummyTransformerModel(width=width, depth=depth)],
          attrs={
              "get_spans": get_spans,
              "hf_model": hf_model,
              "grad_factor": 1.0,
              "flush_cache_chance": 0.0,
              "transformer_config": {},
          },
          dims={"nO": width},
      )
  
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 3 for calling a global function with example usage: DummyTransformer(depth, width, get_spans) and returns: DummyModel