code: def frombuffer(
      buffer: bytes,
      dtype: Optional[paddle.dtype] = float,
      count: Optional[int] = -1,
      offset: Optional[int] = 0,
  ) -> paddle.Tensor:
      dtype_bytes = int(ivy.Dtype(dtype).dtype_bits / 8)
      if str(dtype) == "bool":
          dtype_bytes = 1
      dtype_str = str(dtype)
      struct_format = {
          "bool": "?",
          "int8": "b",
          "int16": "h",
          "int32": "i",
          "int64": "q",
          "uint8": "B",
          "float16": "e",
          "float32": "f",
          "float64": "d",
      }
      ret = []
      for i in range(0, len(buffer), dtype_bytes):
          x = struct.unpack(
              struct_format[dtype_str], buffer[i : i + dtype_bytes]
          )
          ret = ret + list(x)
      if offset > 0:
          offset = int(offset / dtype_bytes)
      if count > -1:
          ret = ret[offset : offset + count]
      else:
          ret = ret[offset:]
      ret = paddle.to_tensor(ret, dtype=dtype)
  
      return ret
  
imports: import struct
  from numbers import Number
  from typing import Union, List, Optional, Sequence, Tuple
  import numpy
  import paddle
  import ivy.functional.backends.paddle
  import ivy
  from ivy.func_wrapper import with_unsupported_device_and_dtypes
  from ivy.functional.ivy.creation import _asarray_to_native_arrays_and_back, _asarray_infer_device, _asarray_handle_nestable, _asarray_infer_dtype, NestedSequence, SupportsBufferProtocol, _asarray_inputs_to_native_shapes, _remove_np_bfloat16
  from .None import backend_version
  from paddle.device import core
label_desc: Use image node_id 23 for calling a global function with example usage: frombuffer(buffer, dtype, count, offset) and returns: ret