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 extract(
      self, serialized_tensor: runtime_pb2.Tensor
  ) -> torch.Tensor:
      shape = torch.Size(serialized_tensor.size)
      if serialized_tensor.dtype == "bfloat16":
          numel = shape.numel()
          if numel > 0 and len(serialized_tensor.buffer) // numel == 4:
              array = np.frombuffer(
                  serialized_tensor.buffer, dtype=np.float32
              )
              tensor = torch.as_tensor(array, dtype=torch.bfloat16)
          else:
              array = np.frombuffer(
                  serialized_tensor.buffer, dtype=np.int16
              )
              # reinterpret_cast from an arbitrary 2-byte type supported by numpy
              tensor = torch.as_tensor(array).view(torch.bfloat16)
      else:
          array = np.frombuffer(
              serialized_tensor.buffer,
              dtype=np.dtype(serialized_tensor.dtype),
          )
          tensor = torch.as_tensor(array)
      return tensor.reshape(shape)
  
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 2 for calling the NoCompression obj's underlying member method code with example usage: obj.extract(serialized_tensor) and returns: tensor