class_docstr: Loads a TensorFlow model and tokenizer.
  
  TensorFlow models can use many different architectures and
  tokenization strategies. This assumes that the model takes an
  np.array of strings as input and returns a tf.Tensor of outputs, as
  is typical with Keras modules. You may need to subclass this for
  models that have dedicated tokenizers or otherwise take input
  differently.
class_docstr_tok: ["Loads","a","TensorFlow","model","and","tokenizer",".","TensorFlow","models","can","use","many","different","architectures","and","tokenization","strategies",".","This","assumes","that","the","model","takes","an","np.array","of","strings","as","input","and","returns","a","tf.Tensor","of","outputs",",","as","is","typical","with","Keras","modules",".","You","may","need","to","subclass","this","for","models","that","have","dedicated","tokenizers","or","otherwise","take","input","differently","."]
code: def get_grad(self, text_input):
      raise NotImplementedError()
  
imports: import numpy
  from .model_wrapper import ModelWrapper
label_desc: Use image node_id 3 for calling the TensorFlowModelWrapper obj's underlying member method code with example usage: obj.get_grad(text_input) without return types