docs: def visualize_activation_with_losses(input_tensor, losses, wrt_tensor=None,
                                       seed_input=None, input_range=(0, 255),
                                       **optimizer_params):
      """Generates the `input_tensor` that minimizes the weighted `losses`. This function is intended for advanced
      use cases where a custom loss is desired.
  
      Args:
          input_tensor: An input tensor of shape: `(samples, channels, image_dims...)` if `image_data_format=
              channels_first` or `(samples, image_dims..., channels)` if `image_data_format=channels_last`.
          wrt_tensor: Short for, with respect to. The gradients of losses are computed with respect to this tensor.
              When None, this is assumed to be the same as `input_tensor` (Default value: None)
          losses: List of ([Loss](vis.losses#Loss), weight) tuples.
          seed_input: Seeds the optimization with a starting image. Initialized with a random value when set to None.
              (Default value = None)
          input_range: Specifies the input range as a `(min, max)` tuple. This is used to rescale the
              final optimized input to the given range. (Default value=(0, 255))
          optimizer_params: The **kwargs for optimizer [params](vis.optimizer#optimizerminimize). Will default to
              reasonable values when required keys are not found.
  
      Returns:
          The model input that minimizes the weighted `losses`.
      """
      # Default optimizer kwargs.
      optimizer_params = utils.add_defaults_to_kwargs({
          'seed_input': seed_input,
          'max_iter': 200,
          'verbose': False
      }, **optimizer_params)
  
      opt = Optimizer(input_tensor, losses, input_range, wrt_tensor=wrt_tensor)
      img = opt.minimize(**optimizer_params)[0]
  
      # If range has integer numbers, cast to 'uint8'
      if isinstance(input_range[0], int) and isinstance(input_range[1], int):
          img = np.clip(img, input_range[0], input_range[1]).astype('uint8')
  
      if K.image_data_format() == 'channels_first':
          img = np.moveaxis(img, 0, -1)
      return img