class_docstr: Interface for tensorflow models.
docstr: Predict on the given input.
  
  Parameters
  ----------
  x: np.ndarray
      A given input that the wrapped model predicts on.
  kwargs: optional
      Keyword arguments passed to tf.keras.Model.predict.
  
  Returns
  --------
  np.ndarray
      predictions of the same dimension and shape as the input, values in the range [0, 1].
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code: def predict(self, x: np.ndarray, **kwargs) -> np.ndarray:
      """
      Predict on the given input.
  
      Parameters
      ----------
      x: np.ndarray
          A given input that the wrapped model predicts on.
      kwargs: optional
          Keyword arguments passed to tf.keras.Model.predict.
  
      Returns
      --------
      np.ndarray
          predictions of the same dimension and shape as the input, values in the range [0, 1].
      """
      # Generally, one should always prefer keras predict to __call__.
      # Reference: https://keras.io/getting_started/faq/#whats-the-difference-between-model-methods-predict-and-call.
      predict_kwargs = self._get_predict_kwargs(**kwargs)
      predict_model = self.get_softmax_arg_model()
  
      return predict_model.predict(x, **predict_kwargs)
  
imports: from __future__ import annotations
  from typing import Dict, Optional, Tuple, List, Union, Generator
  from keras.layers import Dense
  from keras import activations
  from keras import Model
  from keras.models import clone_model
  import numpy
  import tensorflow
  from warnings import warn
  from cachetools import cachedmethod, LRUCache
  import operator
  from quantus.helpers.model.model_interface import ModelInterface
  from quantus.helpers import utils
label_desc: Use image node_id 7 for calling the TensorFlowModel obj's underlying member method code with example usage: obj.predict(x) and returns: predict_model