class_docstr: A dummy compression strategy that preserves the original tensor as is.
class_docstr_tok: ["A","dummy","compression","strategy","that","preserves","the","original","tensor","as","is","."]
code: def compress(
      self,
      tensor: torch.Tensor,
      info: CompressionInfo,
      allow_inplace: bool = False,
  ) -> runtime_pb2.Tensor:
      requires_grad = tensor.requires_grad
      tensor = tensor.detach()
      shape = tensor.shape
      dtype_name = str(tensor.dtype).replace("torch.", "")
      raw_data = tensor
      if tensor.dtype == torch.bfloat16:
          if USE_LEGACY_BFLOAT16:  # legacy mode: convert to fp32
              raw_data = tensor.to(torch.float32)
          else:  # efficient mode: send bfloat16 data directly
              # reinterpret_cast to an arbitrary 2-byte type supported by numpy
              raw_data = tensor.view(torch.int16)
  
      return runtime_pb2.Tensor(
          compression=self.compression_type,
          buffer=raw_data.numpy().tobytes(),
          size=shape,
          dtype=dtype_name,
          requires_grad=requires_grad,
      )
  
imports: import dataclasses
  import os
  import warnings
  from abc import ABC, abstractmethod
  from enum import Enum, auto
  from typing import Any, Optional
  import numpy
  import torch
  from hivemind.proto import runtime_pb2
  from hivemind.utils.tensor_descr import TensorDescriptor
label_desc: Use image node_id 1 for calling the NoCompression obj's underlying member method code with example usage: obj.compress(tensor, info, allow_inplace) and returns: runtime_pb2