class_docstr: Collect data from the loader relevant to the specific task.
  
  This is usually the last stage of the data loader pipeline. Typically keys
  is set to some subset of "img", "gt_semantic_seg".
  
  The "img_meta" item is always populated.  The contents of the "img_meta"
  dictionary depends on "meta_keys". By default this includes:
  
      - "img_shape": shape of the image input to the network as a tuple
          (h, w, c).  Note that images may be zero padded on the bottom/right
          if the batch tensor is larger than this shape.
  
      - "scale_factor": a float indicating the preprocessing scale
  
      - "flip": a boolean indicating if image flip transform was used
  
      - "filename": path to the image file
  
      - "ori_shape": original shape of the image as a tuple (h, w, c)
  
      - "pad_shape": image shape after padding
  
      - "img_norm_cfg": a dict of normalization information:
          - mean - per channel mean subtraction
          - std - per channel std divisor
          - to_rgb - bool indicating if bgr was converted to rgb
  
  Args:
      keys (Sequence[str]): Keys of results to be collected in ``data``.
      meta_keys (Sequence[str], optional): Meta keys to be converted to
          ``mmcv.DataContainer`` and collected in ``data[img_metas]``.
          Default: ``('filename', 'ori_filename', 'ori_shape', 'img_shape',
          'pad_shape', 'scale_factor', 'flip', 'flip_direction',
          'img_norm_cfg')``
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code: def __repr__(self):
      return (
          self.__class__.__name__
          + f"(keys={self.keys}, meta_keys={self.meta_keys})"
      )
  
imports: from collections.abc import Sequence
  import annotator.uniformer.mmcv
  import numpy
  import torch
  from annotator.uniformer.mmcv.parallel import DataContainer
  from ..builder import PIPELINES
label_desc: Use image node_id 3 for calling the Collect obj's underlying member method code with example usage: obj.__repr__() and returns: unknown