code: def _calculate_sum_int(
      batches: List[List[tf.Tensor]],
      model: Union[tf.keras.Model],
      target: Optional[np.ndarray],
      target_paths: np.ndarray,
      n_steps: int,
      nb_samples: int,
      step_sizes: List[float],
      j: int,
  ) -> Union[tf.Tensor, np.ndarray]:
      """
      Calculates the sum of all the terms in the integral from a list of batch gradients.
  
      Parameters
      ----------
      batches
          List of batch gradients.
      model
          `tf.keras` or `keras` model.
      target
          List of targets.
      target_paths
          Targets for each path in the integral.
      n_steps
          Number of steps in the integral.
      nb_samples
          Total number of samples.
      step_sizes
          Step sizes used to calculate the integral.
      j
          Iterates through list of inputs or list of layers.
  
      Returns
      -------
      Sums of the gradients along the chosen path.
      """
      grads = tf.concat(batches[j], 0)
      shape = grads.shape[1:]
      if isinstance(shape, tf.TensorShape):
          shape = tuple(shape.as_list())
  
      # invert sign of gradients for target 0 examples if classifier returns only positive class probability
      if (
          len(model.output_shape) == 1 or model.output_shape[-1] == 1
      ) and target is not None:
          sign = 2 * target_paths - 1
          grads = np.array([s * g for s, g in zip(sign, grads)])
  
      grads = tf.reshape(grads, (n_steps, nb_samples) + shape)
      # sum integral terms and scale attributions
      sum_int = _sum_integral_terms(step_sizes, grads.numpy())
  
      return sum_int
  
imports: import copy
  import logging
  import string
  import warnings
  from enum import Enum
  from typing import Callable, List, Optional, Tuple, Union, cast
  import numpy
  import tensorflow
  from alibi.api.defaults import DEFAULT_DATA_INTGRAD, DEFAULT_META_INTGRAD
  from alibi.api.interfaces import Explainer, Explanation
  from alibi.utils.approximation_methods import approximation_parameters
label_desc: Use image node_id 14 for calling a global function with example usage: _calculate_sum_int(batches, model, target, target_paths, n_steps, nb_samples, step_sizes, j) and returns: sum_int