Using bert-base-uncased instead of default roberta-base inside an en_core_web_trf based pipeline

## Using bert-base-uncased instead of default roberta-base inside an en_core_web_trf based pipeline
On loading the en_core_web_trf model using spacy.load, the nlp.pipeline has the default transformer component (based on roberta-base) loaded before the tagger. I want to replace it with the bert-base-uncased model. I tried the following steps, which seem to give no error. But when parsing a sentence using nlp, it seems to raise a "NoneType object isn't callable" error.

1) Removing the transformer component
`nlp.remove_pipe("transformer")`
`nlp.pipeline (shows the transformer component removed)`

2) Creating a config (based on the example on https://spacy.io/api/transformer)
`config = {
      "model": {
          "@architectures": "spacy-transformers.TransformerModel.v3",
          "name": "D:\\Work\\Tools\\bert-base-uncased",
          "tokenizer_config": {"use_fast": True},
          "transformer_config": {"output_attentions": True},
          "mixed_precision": True,
          "grad_scaler_config": {"init_scale": 32768}
      }
 }`

3) Adding the desired transformer model (using the config) in the  pipeline
`trf = nlp.add_pipe("transformer", config=config, before="tagger")`

4) Checking nlp.pipeline (Shows a new transformer as part of the pipeline)

5) Checking on a sentence leads to a NoneType object isn't callable error
`s = 'John went to London.'`
`doc = nlp(s)`

Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "D:\Work\Tools\envs\env_main2\lib\site-packages\spacy\language.py", line 1018, in __call__
    error_handler(name, proc, [doc], e)
  File "D:\Work\Tools\envs\env_main2\lib\site-packages\spacy\util.py", line 1618, in raise_error
    raise e
  File "D:\Work\Tools\envs\env_main2\lib\site-packages\spacy\language.py", line 1013, in __call__
    doc = proc(doc, **component_cfg.get(name, {}))  # type: ignore[call-arg]
  File "D:\Work\Tools\envs\env_main2\lib\site-packages\spacy_transformers\pipeline_component.py", line 192, in __call__
    outputs = self.predict([doc])
  File "D:\Work\Tools\envs\env_main2\lib\site-packages\spacy_transformers\pipeline_component.py", line 228, in predict
    activations = self.model.predict(docs)
  File "D:\Work\Tools\envs\env_main2\lib\site-packages\thinc\model.py", line 315, in predict
    return self._func(self, X, is_train=False)[0]
  File "D:\Work\Tools\envs\env_main2\lib\site-packages\spacy_transformers\layers\transformer_model.py", line 177, in forward
    batch_encoding = huggingface_tokenize(tokenizer, [span.text for span in flat_spans])
  File "D:\Work\Tools\envs\env_main2\lib\site-packages\spacy_transformers\layers\transformer_model.py", line 278, in huggingface_tokenize
    padding="longest",
TypeError: 'NoneType' object is not callable

Assuming the bert-base-uncased model is available and downloaded in a suitable local directory, is this a right way to use a transformer model different from the default roberta-base? If not, is there a better way to achieve the desired setting? And if this is a possible way, can some light be thrown on why the NoneType error?

## Your Environment
- **spaCy version:** 3.2.0
- **Platform:** Windows-10-10.0.14393-SP0
- **Python version:** 3.7.11
- **Pipelines:** en_core_web_md (3.2.0), en_core_web_trf (3.2.0)


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Using bert-base-uncased instead of default roberta-base inside an en_core_web_trf based pipeline #10281