class_docstr: Depth estimation pipeline using any `AutoModelForDepthEstimation`. This pipeline predicts the depth of an image.
  
  This depth estimation pipeline can currently be loaded from [`pipeline`] using the following task identifier:
  `"depth-estimation"`.
  
  See the list of available models on [huggingface.co/models](https://huggingface.co/models?filter=depth-estimation).
code: def postprocess(self, model_outputs):
      predicted_depth = model_outputs.predicted_depth
      prediction = torch.nn.functional.interpolate(
          predicted_depth.unsqueeze(1),
          size=self.image_size[::-1],
          mode="bicubic",
          align_corners=False,
      )
      output = prediction.squeeze().cpu().numpy()
      formatted = (output * 255 / np.max(output)).astype("uint8")
      depth = Image.fromarray(formatted)
      output_dict = {}
      output_dict["predicted_depth"] = predicted_depth
      output_dict["depth"] = depth
      return output_dict
  
file_path: H2O/h2o_flexgen/benchmark/third_party/transformers/src/transformers/pipelines/depth_estimation.py
imports: from typing import List, Union
  import numpy
  from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
  from .base import PIPELINE_INIT_ARGS, Pipeline
label_desc: Use image node_id 6 for calling the DepthEstimationPipeline obj's underlying member method code with example usage: obj.postprocess(model_outputs) and returns: output_dict