code: def _main(
      objective_f,
      model,
      layer,
      channel_indices,
      alpha=False,
      negative=False,
      decorrelation_matrix=None,
      n_steps=128,
      parallel_transforms=16,
      lr=0.05,
      override_gradients=True,
  ):
      if not isinstance(channel_indices, (tuple, list)):
          channel_indices = [channel_indices]
  
      if isinstance(layer, Layer):
          layer_name = layer.name
      elif isinstance(layer, str):
          layer_name = layer
      else:
          raise ValueError(
              "layer argument can be either a Layer object or str"
          )
  
      sign = -1 if negative else 1
      batch = len(channel_indices)
      w, h, _ = model.image_shape
  
      with tf.Graph().as_default(), tf.Session() as sess:
          # Transformation Robustness
          transforms = transform.standard_transforms + [
              transform.crop_or_pad_to(w, h)
          ]
          if alpha:
              transforms += [transform.collapse_alpha_random()]
          transform_f = render.make_transform_f(transforms)
  
          # Parameterization
          image_t = param.image(
              w, h, fft=True, decorrelate=True, alpha=alpha, batch=batch
          )
          param_t = transform_f(image_t)
  
          # Gradient Overrides
          if override_gradients:
              with overrides.relu_overrides():
                  T = render.import_model(model, param_t, image_t)
          else:
              T = render.import_model(model, param_t, image_t)
  
          # Objective
          if decorrelation_matrix is None:
              objs = [
                  sign * objective_f(layer_name, i, batch=b)
                  for b, i in enumerate(channel_indices)
              ]
          else:
              raise NotImplementedError
  
          reported_obj = tf.stack([o(T) for o in objs])
  
          obj = sum(objs)(T)
  
          if alpha:
              obj *= 1.0 - tf.reduce_mean(image_t[..., -1])
              obj -= 0.1 * objectives.blur_alpha_each_step()(T)
  
          # Optimization
  
          optimization = tf.train.AdamOptimizer(lr).minimize(-obj)
          tf.global_variables_initializer().run()
          losses = np.zeros((batch, n_steps))
          for step in range(n_steps):
              _, loss = sess.run([optimization, reported_obj])
              losses[:, step] = loss
  
          # Evaluation
          visualization = image_t.eval()
  
      if batch == 1:
          return (visualization, losses)
      else:
          return list(zip(visualization, losses))
  
file_path: lucid/lucid/recipes/feature_visualization.py
imports: from timeit import default_timer
  import tensorflow
  import numpy
  from lucid.optvis import render
  from lucid.optvis import param
  from lucid.optvis import transform
  from lucid.optvis import objectives
  from lucid.optvis import overrides
  from lucid.misc.io import show, load
  from lucid.modelzoo.vision_base import Layer
label_desc: Use image node_id 3 for calling a global function with example usage: _main(objective_f, model, layer, channel_indices, alpha, negative, decorrelation_matrix, n_steps, parallel_transforms, lr, override_gradients) and returns: visualization,  losses, list