code: import torch
  import numpy as np
  from mannequinchallenge.options.train_options import TrainOptions
  from mannequinchallenge.loaders import aligned_data_loader
  from mannequinchallenge.models import pix2pix_model
  
  model = None
  
  class DictX(dict):
      def __getattr__(self, key):
          try:
              return self[key]
          except KeyError as k:
              raise AttributeError(k)
  
      def __setattr__(self, key, value):
          self[key] = value
  
      def __delattr__(self, key):
          try:
              del self[key]
          except KeyError as k:
              raise AttributeError(k)
  
      def __repr__(self):
          return '<DictX ' + dict.__repr__(self) + '>'
  
  opt = DictX({
      'input': 'single_view',
      "simple_keypoints": 0,
      "mode": "Ours_Bilinear",
      "human_data_term": 0,
      'batchSize': 8,
      'loadSize': 286,
      'fineSize': 256,
      'output_nc': 3,
      'ngf': 64,
      'ndf': 64,
      'which_model_netG': 'unet_256',
      'gpu_ids': '0,1,2,3',
      'name': 'test_local',
      'model': 'pix2pix',
      'nThreads': 2,
      'checkpoints_dir': './monoculardepth/mannequinchallenge/checkpoints/',
      'norm': 'instance',
      'display_winsize': 256,
      'display_id': 1, 
      'identity': 0,
      'max_dataset_size': float("inf"),
      'display_freq': 100,
      'print_freq': 100,
      'save_latest_freq': 5000,
      'save_epoch_freq': 5,
      'phase': 'train',
      'which_epoch': 'latest',
      'niter': 100,
      'niter_decay': 100,
      'lr_decay_epoch': 8,
      'lr_policy': 'step',
      'beta1': 0.5,
      'lr': 0.0004,
      'lambda_A': 10.0,
      'lambda_B': 10.0,
      'pool_size': 50,
      'isTrain': False
  })
  
  def infer_depth(img):
      global model
      BATCH_SIZE = 1
  
      # opt = TrainOptions().parse()  # set CUDA_VISIBLE_DEVICES before import torch
  
      video_data_loader = aligned_data_loader.PLARRDataLoader(img, BATCH_SIZE)
      video_dataset = video_data_loader.load_data()
  
      if model == None:
          model = pix2pix_model.Pix2PixModel(opt)
          model.switch_to_eval()
  
      if torch.cuda.is_available():
          torch.backends.cudnn.enabled = True
          torch.backends.cudnn.benchmark = True
  
      for i, data in enumerate(video_dataset):
          stacked_img = data[0]
          disp_img =  model.run_PLARR(stacked_img)
          disp_img = disp_img.resize(img.size)
          disp_array = np.array(disp_img)
          return disp_array, disp_img
apis: numpy.array torch.cuda.is_available