original_docstring: 
      loads images and mask as a numpy array, reshapes images and mask to 4 channels
      and normalzes image data
  
      Args:
          image_path:the path of the npy array containing images
                     type: String
          mask_path:      the path to npy array containing masks
                     type: String
      Return:
          numpy array of augmented mask and image
      
docstring: loads images and mask as a numpy array, reshapes images and mask to 4 channels
  and normalzes image data
docstring_tokens: loads images and mask as a numpy array reshapes images and mask to 4 channels and normalzes image data
code: def augment_data(image_path,mask_path,mode):
      '''
      loads images and mask as a numpy array, reshapes images and mask to 4 channels
      and normalzes image data
  
      Args:
          image_path:the path of the npy array containing images
                     type: String
          mask_path:      the path to npy array containing masks
                     type: String
      Return:
          numpy array of augmented mask and image
      '''
      image_array = np.load(image_path)
      images = image_array[...,np.newaxis]
      image_data_array = images.astype('float32')
      data_images = image_data_array - np.mean(image_data_array)
      data_images = data_images / np.std(image_data_array)
  
      if(mode=="fit"):
          mask_array = np.load(mask_path)
          masks = mask_array[...,np.newaxis]
          mask_images = masks.astype('float32')
          return data_images, mask_images
      else:
          return data_images
code_tokens: def augment_data ( image_path , mask_path , mode ) : image_array = np . load ( image_path ) images = image_array [ ... , np . newaxis ] image_data_array = images . astype ( 'float32' ) data_images = image_data_array - np . mean ( image_data_array ) data_images = data_images / np . std ( image_data_array ) if ( mode == "fit" ) : mask_array = np . load ( mask_path ) masks = mask_array [ ... , np . newaxis ] mask_images = masks . astype ( 'float32' ) return data_images , mask_images else : return data_images
short_docstring: loads images and mask as a numpy array, reshapes images and mask to 4 channels
  and normalzes image data
short_docstring_tokens: loads images and mask as a numpy array reshapes images and mask to 4 channels and normalzes image data
comment: '''
      loads images and mask as a numpy array, reshapes images and mask to 4 channels
      and normalzes image data
  
      Args:
          image_path:the path of the npy array containing images
                     type: String
          mask_path:      the path to npy array containing masks
                     type: String
      Return:
          numpy array of augmented mask and image
      '''