class_docstr: This is a 1-element tensor represents unspecialized python float/int.
class_docstr_tok: ["This","is","a","1-element","tensor","represents","unspecialized","python","float\/int","."]
code: def __init__(
      self,
      proxy: torch.fx.Proxy,
      *,
      raw_value=None,
      need_unwrap=True,
      **kwargs,
  ):
      super().__init__(proxy, **kwargs)
      self.raw_value = raw_value
      self.need_unwrap = need_unwrap
  
imports: import functools
  import inspect
  import operator
  import types
  from typing import Dict, List
  import sympy
  import torch._numpy
  import torch.fx
  import torch.random
  from torch._dynamo import compiled_autograd
  from torch.fx.experimental.symbolic_shapes import guard_scalar, GuardOnDataDependentSymNode, has_free_symbols, is_symbolic, SymTypes
  from ..None import config, variables
  from .._trace_wrapped_higher_order_op import trace_wrapped
  from ..exc import unimplemented, UserError, UserErrorType
  from ..guards import GuardBuilder, install_guard
  from ..source import AttrSource
  from ..utils import fqn, get_custom_getattr, get_fake_value, get_real_value, guard_if_dyn, object_has_getattribute, product, proxy_args_kwargs, tensortype_to_dtype
  from .base import VariableTracker
  from .constant import ConstantVariable
  from .lists import SizeVariable
label_desc: Use image node_id 1 to create a new UnspecializedPythonVariable object from inherited base classes: TensorVariable with example: obj = UnspecializedPythonVariable(proxy)