class_docstr: Generate random planckian jitter parameters for a batch of images.
code: def forward(
      self, batch_shape: Tuple[int, ...], same_on_batch: bool = False
  ) -> Dict[str, torch.Tensor]:
      batch_size = batch_shape[0]
      _common_param_check(batch_size, same_on_batch)
      pl_idx = _adapted_rsampling(
          (batch_size,), self.pl_idx_dist, same_on_batch
      )
  
      return {"idx": pl_idx.long()}
  
file_path: kornia/kornia/augmentation/random_generator/_2d/planckian_jitter.py
imports: from typing import Dict, List, Tuple
  import torch
  from kornia.augmentation.random_generator.base import RandomGeneratorBase, UniformDistribution
  from kornia.augmentation.utils import _adapted_rsampling, _common_param_check, _joint_range_check, _range_bound
label_desc: Use image node_id 3 for calling the PlanckianJitterGenerator obj's underlying member method code with example usage: obj.forward(batch_shape, same_on_batch) and returns: dict