code: def adjust_brightness(
      tens: Tensor, coeff: float, rgb: bool = True
  ) -> Tensor:
      r"""
          Adjust the brightness of an image.
  
      Args:
          tens (Tensor) : Any regular ``caer.Tensor``.
          coeff (int): Coefficient value.
              - ``coeff < 1``, the image is darkened.
              - ``coeff = 1``, the image is unchanged.
              - ``coeff > 1``, the image is lightened.
          rgb (bool): Operate on RGB images. Default: True.
  
      Returns:
          Tensor of shape ``(height, width, channels)``.
  
      Examples::
  
          >> tens = caer.data.sunrise(rgb=True)
          >> filtered = caer.transforms.adjust_brightness(tens, coeff=1.4, rgb=True)
          >> filtered
          (427, 640, 3)
  
      """
      tens = to_tensor(tens, enforce_tensor=True)
      tens = _hls(tens)
      cspace = tens.cspace
  
      tens = np.array(tens, dtype=np.float64)
      tens[:, :, 1] = (
          tens[:, :, 1] * coeff
      )  ## scale pixel values up or down for channel 1 (for lightness)
  
      if coeff > 1:
          tens[:, :, 1][
              tens[:, :, 1] > 255
          ] = 255  # Set all values > 255 to 255
      else:
          tens[:, :, 1][tens[:, :, 1] < 0] = 0
  
      return to_tensor(tens, cspace=cspace, dtype=np.uint8)
  
file_path: caer/caer/transforms/color.py
imports: import numpy
  import random
  import cv2
  import math
  from ..coreten import Tensor, to_tensor
  from ..annotations import Union, List, Tuple
  from .functional import is_list, _hls, _exposure_process
label_desc: Use image node_id 1 for calling a global function with example usage: adjust_brightness(tens, coeff, rgb) and returns: to_tensor