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 interpolation paths for batch of images.
  
  Args:
      images (numpy.ndarray): 4D-Tensor of images with shape (N, H, W, 3)
      n_steps (int): Number of steps in the path
  
  Returns:
      numpy.ndarray: Interpolation paths for each image with shape (N * n_steps, H, W, 3)
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code: def generate_interpolations(images, n_steps):
      """
      Generate interpolation paths for batch of images.
  
      Args:
          images (numpy.ndarray): 4D-Tensor of images with shape (N, H, W, 3)
          n_steps (int): Number of steps in the path
  
      Returns:
          numpy.ndarray: Interpolation paths for each image with shape (N * n_steps, H, W, 3)
      """
      baseline = np.zeros(images.shape[1:])
  
      return np.concatenate(
          [
              IntegratedGradients.generate_linear_path(
                  baseline, image, n_steps
              )
              for image in images
          ]
      )
  
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 3 for calling the IntegratedGradients obj's underlying member method code with example usage: obj.generate_interpolations(images, n_steps) and returns: np