class_docstr: A  layer wrapper for random LTD
class_docstr_tok: ["A","layer","wrapper","for","random","LTD"]
code: def forward(self, hidden_states, **kwargs) -> Tensor:
      if self.random_ltd_scheduler is not None:
          self.reserved_length = (
              self.random_ltd_scheduler.get_current_seq()
          )
          self.get_hidden_tensor_shape(hidden_states)
      if (
          self.training
          and self.random_ltd_scheduler is not None
          and self.reserved_length < self.curr_seq
      ):
          if self.mask_name is not None:
              mask = kwargs[self.mask_name]
          else:
              mask = None
          if self.random_ltd_layer_id == 0:
              (
                  sampled_indices,
                  part_attention_mask,
              ) = self.index_generator(
                  self.reserved_length,
                  self.curr_seq,
                  self.curr_micro_batch,
                  self.random_ltd_num_layer,
                  hidden_states.device,
                  mask,
              )
              self.random_ltd_scheduler.state[
                  RANDOM_LTD_SAMPLE_INDEX
              ] = sampled_indices
              self.random_ltd_scheduler.state[
                  RANDOM_LTD_ATTENTION_MASK
              ] = part_attention_mask
          else:
              sampled_indices = self.random_ltd_scheduler.state[
                  RANDOM_LTD_SAMPLE_INDEX
              ]
              part_attention_mask = self.random_ltd_scheduler.state[
                  RANDOM_LTD_ATTENTION_MASK
              ]
  
          hidden_states, part_hidden_states = GatherTokens.apply(
              hidden_states,
              sampled_indices[self.random_ltd_layer_id, :, :],
              self.batch_first,
          )
          if self.mask_name is not None:
              if self.model_type == "encoder":
                  kwargs[self.mask_name] = part_attention_mask[
                      self.random_ltd_layer_id
                  ]
              else:
                  kwargs[self.mask_name] = part_attention_mask
  
          outputs = self.random_ltd_layer(part_hidden_states, **kwargs)
  
          if isinstance(outputs, tuple):
              hidden_states = ScatterTokens.apply(
                  hidden_states,
                  outputs[0],
                  sampled_indices[self.random_ltd_layer_id, :, :],
                  self.batch_first,
              )
              my_list = list(outputs)
              my_list[0] = hidden_states
              return tuple(my_list)
          elif isinstance(outputs, Tensor):
              hidden_states = ScatterTokens.apply(
                  hidden_states,
                  outputs,
                  sampled_indices[self.random_ltd_layer_id, :, :],
                  self.batch_first,
              )
              return hidden_states
          else:
              logger.warning(
                  "************For now, we only support tuple and tensor output.  \
                         You need to adjust the output according to the layer in your model************"
              )
              raise NotImplementedError
      else:
          return self.random_ltd_layer(hidden_states, **kwargs)
  
file_path: DeepSpeed/deepspeed/runtime/data_pipeline/data_routing/basic_layer.py
imports: from deepspeed.utils import logger
  from torch import Tensor
  from torch.nn import Module
  from ..constants import *
  from deepspeed.ops.random_ltd.dropping_utils import gpt_sample_tokens, bert_sample_tokens, GatherTokens, ScatterTokens
label_desc: Use image node_id 5 for calling the RandomLayerTokenDrop obj's underlying member method code with example usage: obj.forward(hidden_states) and returns: self, tuple, hidden_states