class_docstr: A DoFn that loads tfjs models and predicts.
class_docstr_tok: ["A","DoFn","that","loads","tfjs","models","and","predicts","."]
code: def __init__(
      self,
      eval_config: config_pb2.EvalConfig,
      eval_shared_models: Dict[str, types.EvalSharedModel],
  ) -> None:
      super().__init__(
          {k: v.model_loader for k, v in eval_shared_models.items()}
      )
      self._eval_config = eval_config
      self._src_model_paths = {
          k: v.model_path for k, v in eval_shared_models.items()
      }
  
imports: import collections
  import copy
  import json
  import os
  import subprocess
  import tempfile
  from typing import Dict, List, NamedTuple, Sequence, Union
  import uuid
  import apache_beam
  import numpy
  import tensorflow
  from tensorflow_model_analysis import constants
  from tensorflow_model_analysis.api import types
  from tensorflow_model_analysis.extractors import extractor
  from tensorflow_model_analysis.extractors.tfjs_predict_extractor_util import get_tfjs_binary
  from tensorflow_model_analysis.proto import config_pb2
  from tensorflow_model_analysis.utils import model_util
  from tensorflow_model_analysis.utils import util
label_desc: Use image node_id 1 to create a new _TFJSPredictionDoFn object from inherited base classes: model_util with example: obj = _TFJSPredictionDoFn(eval_config, eval_shared_models)