class_docstr: Depth estimation pipeline using any `AutoModelForDepthEstimation`. This pipeline predicts the depth of an image.
  
  Example:
  
  ```python
  >>> from transformers import pipeline
  
  >>> depth_estimator = pipeline(task="depth-estimation", model="Intel/dpt-large")
  >>> output = depth_estimator("http://images.cocodataset.org/val2017/000000039769.jpg")
  >>> # This is a tensor with the values being the depth expressed in meters for each pixel
  >>> output["predicted_depth"].shape
  torch.Size([1, 384, 384])
  ```
  
  Learn more about the basics of using a pipeline in the [pipeline tutorial](../pipeline_tutorial)
  
  
  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 _sanitize_parameters(self, timeout=None, **kwargs):
      preprocess_params = {}
      if timeout is not None:
          preprocess_params["timeout"] = timeout
      return preprocess_params, {}, {}
  
file_path: 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 3 for calling the DepthEstimationPipeline obj's underlying member method code with example usage: obj._sanitize_parameters(timeout) and returns: preprocess_params,  dict,  dict