code: def frombuffer(
      buffer: bytes,
      dtype: Optional[torch.dtype] = float,
      count: Optional[int] = -1,
      offset: Optional[int] = 0,
  ) -> torch.Tensor:
      buffer_copy = copy.deepcopy(buffer)
      dtype = ivy.as_native_dtype(dtype)
  
      return torch.frombuffer(
          buffer_copy, dtype=dtype, count=count, offset=offset
      )
  
imports: import copy
  from numbers import Number
  from typing import Union, List, Optional, Sequence, Tuple
  import numpy
  import torch
  from torch import Tensor
  import ivy
  from ivy.func_wrapper import with_unsupported_dtypes, with_unsupported_device_and_dtypes
  from ivy.functional.ivy.creation import _asarray_to_native_arrays_and_back, _asarray_infer_device, _asarray_infer_dtype, _asarray_handle_nestable, NestedSequence, SupportsBufferProtocol, _asarray_inputs_to_native_shapes, _remove_np_bfloat16
  from .None import backend_version
label_desc: Use image node_id 26 for calling a global function with example usage: frombuffer(buffer, dtype, count, offset) and returns: torch