code: def test_optimize_with_custom_input(mocker, conv_layer, model):
      mocker.spy(model, "forward")
      g_ascent = GradientAscent(model)
  
      custom_input = np.uint8(np.random.uniform(150, 180, (64, 64, 3)))
      custom_input = apply_transforms(custom_input, size=64)
  
      g_ascent.optimize(conv_layer, 0, input_=custom_input, num_iter=1)
  
      model.forward.assert_called_with(custom_input)
  
imports: import inspect
  import pytest
  from os import path
  import numpy
  import torchvision.models
  from flashtorch.activmax import GradientAscent
  from flashtorch.utils import apply_transforms
label_desc: Use image node_id 6 for calling a global function with example usage: test_optimize_with_custom_input(mocker, conv_layer, model) without return types