code: def init_checkpointed_activations_memory_buffer(
      micro_batch_size,
      max_position_embeddings,
      hidden_size,
      num_layers,
      tensor_model_parallel_size,
      checkpoint_num_layers,
      fp16,
  ):
      """Initializ the memory buffer for the checkpointed activations."""
  
      per_layer = (
          micro_batch_size
          * max_position_embeddings
          * hidden_size
          // tensor_model_parallel_size
      )
      assert (
          num_layers % checkpoint_num_layers == 0
      ), "number of layers is not divisible by checkpoint-num-layers"
      num_checkpointer_layers = num_layers // checkpoint_num_layers
      numel = per_layer * num_checkpointer_layers
      dtype = torch.half
      if not fp16:
          dtype = torch.float
  
      global _CHECKPOINTED_ACTIVATIONS_MEMORY_BUFFER
      assert (
          _CHECKPOINTED_ACTIVATIONS_MEMORY_BUFFER is None
      ), "checkpointed activations memory buffer is already allocated."
      _CHECKPOINTED_ACTIVATIONS_MEMORY_BUFFER = allocate_mem_buff(
          "checkpointed activations", numel, dtype, track_usage=False
      )
  
imports: import contextlib
  import torch
  from torch import _C
  from torch.cuda import _lazy_call, device
  from torch.utils.checkpoint import detach_variable
  from apex.transformer.parallel_state import get_tensor_model_parallel_rank
  from apex.transformer.tensor_parallel.memory import allocate_mem_buff
  from apex.transformer.utils import split_tensor_into_1d_equal_chunks
  from apex.transformer.utils import gather_split_1d_tensor
label_desc: Use image node_id 1 for calling a global function with example usage: init_checkpointed_activations_memory_buffer(micro_batch_size, max_position_embeddings, hidden_size, num_layers, tensor_model_parallel_size, checkpoint_num_layers, fp16) without return types