class_docstr: Perform Integrated Gradients algorithm for a given input
  
  Paper: [Axiomatic Attribution for Deep Networks](https://arxiv.org/pdf/1703.01365.pdf)
class_docstr_tok: ["Perform","Integrated","Gradients","algorithm","for","a","given","input","Paper",":","[","Axiomatic","Attribution","for","Deep","Networks","]","(","https",":","\/\/arxiv.org\/pdf\/1703.01365.pdf",")"]
docstr: Generate the interpolation path between the baseline image and the target image.
  
  Args:
      baseline (numpy.ndarray): Reference image
      target (numpy.ndarray): Target image
      n_steps (int): Number of steps in the path
  
  Returns:
      List(np.ndarray): List of images for each step
docstr_tok: ["Generate","the","interpolation","path","between","the","baseline","image","and","the","target","image",".","Args",":","baseline","(","numpy.ndarray",")",":","Reference","image","target","(","numpy.ndarray",")",":","Target","image","n_steps","(","int",")",":","Number","of","steps","in","the","path","Returns",":","List","(","np.ndarray",")",":","List","of","images","for","each","step"]
code: def generate_linear_path(baseline, target, n_steps):
      """
      Generate the interpolation path between the baseline image and the target image.
  
      Args:
          baseline (numpy.ndarray): Reference image
          target (numpy.ndarray): Target image
          n_steps (int): Number of steps in the path
  
      Returns:
          List(np.ndarray): List of images for each step
      """
      return [
          baseline + (target - baseline) * index / (n_steps - 1)
          for index in range(n_steps)
      ]
  
file_path: tf-explain/tf_explain/core/integrated_gradients.py
imports: import numpy
  import tensorflow
  from tf_explain.utils.display import grid_display
  from tf_explain.utils.image import transform_to_normalized_grayscale
  from tf_explain.utils.saver import save_grayscale
label_desc: Use image node_id 4 for calling the IntegratedGradients obj's underlying member method code with example usage: obj.generate_linear_path(baseline, target, n_steps) and returns: unknown