class_docstr: Masked language modeling prediction pipeline using any `ModelWithLMHead`. See the [masked language modeling
  examples](../task_summary#masked-language-modeling) for more information.
  
  Example:
  
  ```python
  >>> from transformers import pipeline
  
  >>> fill_masker = pipeline(model="bert-base-uncased")
  >>> fill_masker("This is a simple [MASK].")
  [{'score': 0.042, 'token': 3291, 'token_str': 'problem', 'sequence': 'this is a simple problem.'}, {'score': 0.031, 'token': 3160, 'token_str': 'question', 'sequence': 'this is a simple question.'}, {'score': 0.03, 'token': 8522, 'token_str': 'equation', 'sequence': 'this is a simple equation.'}, {'score': 0.027, 'token': 2028, 'token_str': 'one', 'sequence': 'this is a simple one.'}, {'score': 0.024, 'token': 3627, 'token_str': 'rule', 'sequence': 'this is a simple rule.'}]
  ```
  
  Learn more about the basics of using a pipeline in the [pipeline tutorial](../pipeline_tutorial)
  
  This mask filling pipeline can currently be loaded from [`pipeline`] using the following task identifier:
  `"fill-mask"`.
  
  The models that this pipeline can use are models that have been trained with a masked language modeling objective,
  which includes the bi-directional models in the library. See the up-to-date list of available models on
  [huggingface.co/models](https://huggingface.co/models?filter=fill-mask).
  
  <Tip>
  
  This pipeline only works for inputs with exactly one token masked. Experimental: We added support for multiple
  masks. The returned values are raw model output, and correspond to disjoint probabilities where one might expect
  joint probabilities (See [discussion](https://github.com/huggingface/transformers/pull/10222)).
  
  </Tip>
  
  <Tip>
  
  This pipeline now supports tokenizer_kwargs. For example try:
  
  ```python
  >>> from transformers import pipeline
  
  >>> fill_masker = pipeline(model="bert-base-uncased")
  >>> tokenizer_kwargs = {"truncation": True}
  >>> fill_masker(
  ...     "This is a simple [MASK]. " + "...with a large amount of repeated text appended. " * 100,
  ...     tokenizer_kwargs=tokenizer_kwargs,
  ... )
  ```
  
  
  </Tip>
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code: def _forward(self, model_inputs):
      model_outputs = self.model(**model_inputs)
      model_outputs["input_ids"] = model_inputs["input_ids"]
      return model_outputs
  
file_path: transformers/src/transformers/pipelines/fill_mask.py
imports: from typing import Dict
  import numpy
  from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
  from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline, PipelineException
label_desc: Use image node_id 5 for calling the FillMaskPipeline obj's underlying member method code with example usage: obj._forward(model_inputs) and returns: model_outputs