code: def _snapshot(device: Union[Device, int] = None):
      """Save a snapshot of CUDA memory state at the time it was called.
  
      The state is represented as a dictionary with the following structure.
  
      .. code-block:: python
  
          class Snapshot(TypedDict):
              segments : List[Segment]
              device_traces: List[List[TraceEntry]]
  
          class Segment(TypedDict):
              # Segments are memory returned from a cudaMalloc call.
              # The size of reserved memory is the sum of all Segments.
              # Segments are cached and reused for future allocations.
              # If the reuse is smaller than the segment, the segment
              # is split into more then one Block.
              # empty_cache() frees Segments that are entirely inactive.
              address: int
              total_size: int #  cudaMalloc'd size of segment
              stream: int
              segment_type: Literal['small', 'large'] # 'large' (>1MB)
              allocated_size: int # size of memory in use
              active_size: int # size of memory in use or in active_awaiting_free state
              blocks : List[Block]
  
          class Block(TypedDict):
              # A piece of memory returned from the allocator, or
              # current cached but inactive.
              size: int
              requested_size: int # size requested during malloc, may be smaller than
                                  # size due to rounding
              address: int
              state: Literal['active_allocated', # used by a tensor
                          'active_awaiting_free', # waiting for another stream to finish using
                                                  # this, then it will become free
                          'inactive',] # free for reuse
              frames: List[Frame] # stack trace from where the allocation occurred
  
          class Frame(TypedDict):
                  filename: str
                  line: int
                  name: str
  
          class TraceEntry(TypedDict):
              # When `torch.cuda.memory._record_memory_history()` is enabled,
              # the snapshot will contain TraceEntry objects that record each
              # action the allocator took.
              action: Literal[
              'alloc'  # memory allocated
              'free_requested', # the allocated received a call to free memory
              'free_completed', # the memory that was requested to be freed is now
                              # able to be used in future allocation calls
              'segment_alloc', # the caching allocator ask cudaMalloc for more memory
                              # and added it as a segment in its cache
              'segment_free',  # the caching allocator called cudaFree to return memory
                              # to cuda possibly trying free up memory to
                              # allocate more segments or because empty_caches was called
              'oom',          # the allocator threw an OOM exception. 'size' is
                              # the requested number of bytes that did not succeed
              'snapshot'      # the allocator generated a memory snapshot
                              # useful to coorelate a previously taken
                              # snapshot with this trace
              ]
              addr: int # not present for OOM
              frames: List[Frame]
              size: int
              stream: int
              device_free: int # only present for OOM, the amount of
                              # memory cuda still reports to be free
  
      Returns:
          The Snapshot dictionary object
      """
      return _C._cuda_memorySnapshot()
  
imports: import collections
  import contextlib
  import ctypes
  import pickle
  import sys
  import warnings
  from inspect import signature
  from typing import Any, Dict, Optional, Tuple, Union
  import torch
  from torch import _C
  from torch.types import Device
  from .None import _get_device_index, _get_nvml_device_index, _lazy_init, is_initialized
  from ._memory_viz import memory, segments
  from ._utils import _dummy_type
label_desc: Use image node_id 26 for calling a global function with example usage: _snapshot(device) and returns: _C