class_docstr: Question Answering Jax model based on TyDiQA Dataset.
docstr: Predict the answer given the question and context.
docstr_tok: ["Predict","the","answer","given","the","question","and","context","."]
code: def predict(
      self, inputs: Iterable[_JsonDict], **kw
  ) -> Iterable[_JsonDict]:
      """Predict the answer given the question and context."""
      prediction_output: list[_JsonDict] = []
  
      for inp in inputs:
          tokenized_text = self.tokenizer(
              inp["question"],
              inp["context"],
              return_tensors="jax",
              padding=True,
          )
          results = self.model(
              **tokenized_text, output_hidden_states=True
          )
          answer_start_index = results.start_logits.argmax()
          answer_end_index = results.end_logits.argmax()
          predict_answer_tokens = tokenized_text.input_ids[
              0, answer_start_index : answer_end_index + 1
          ]
  
          # get id's for question & context
          tokens = np.asarray(tokenized_text["input_ids"])
          # convert id's to tokens
          total_tokens = self.tokenizer.convert_ids_to_tokens(tokens[0])
          # split by question & context
          slicer_question, slicer_context = self._segment_slicers(
              total_tokens
          )
          # get embeddings
          embeddings = results.hidden_states[0][0]
          # gradient
          gradient = results.hidden_states[-1][0]
  
          prediction_output.append(
              {
                  "generated_text": self.tokenizer.decode(
                      predict_answer_tokens
                  ),
                  "answers_text": inp["answers_text"],
                  # Embeddings come from the first token of the last layer.
                  "cls_emb": results.hidden_states[-1][:, 0][0],
                  "tokens_question": total_tokens[slicer_question],
                  "tokens_context": total_tokens[slicer_context],
                  "grad_class": None,
                  "tokens_embs_question": np.asarray(
                      embeddings[slicer_question]
                  ),
                  "token_grad_context": np.asarray(
                      embeddings[slicer_context]
                  ),
                  "tokens_grad_question": np.asarray(
                      gradient[slicer_question]
                  ),
                  "tokens_embs_context": np.asarray(
                      gradient[slicer_context]
                  ),
              }
          )
  
      return prediction_output
  
file_path: lit/lit_nlp/examples/models/tydi.py
imports: from collections.abc import Iterable
  from lit_nlp.api import model
  from lit_nlp.api import types
  from lit_nlp.examples.datasets import question_answering
  import numpy
  import transformers
label_desc: Use image node_id 4 for calling the TyDiModel obj's underlying member method code with example usage: obj.predict(inputs) and returns: prediction_output