code: def __init__(self, size, dtype, device):
      self.buffer = torch.zeros(size, dtype=dtype, device=device)
  
      # address to contiguous size available
      self.contiguous_sizes = {}
  
      self.contiguous_sizes[0] = size
  
      # tensor id to its address
      self.tensor_addresses = {}
  
      # tensor address to its size
      self.tensor_sizes = {}
  
      # tensor address to ids
      self.tensor_ids = {}
  
      # id to tensors
      self.tensor_map = {}
  
      # id to params. Maps each tensor buffer to list of parameters that uses it
      self.id_to_params = {}
  
      self.total_size = size
      self.total_free = size
      self.largest_contiguous = size
      self.max_allocated = 0
  
      self.count = 0
  
imports: import torch
  from deepspeed import comm
label_desc: Use image node_id 1 to create a new ContiguousMemoryAllocator object from inherited base classes: object with example: obj = ContiguousMemoryAllocator(size, dtype, device)