code: def eval(cls, *args):
      assert len(args) % 2 == 0
      dim = len(args) // 2
      # TODO: it is possible to make progress evaluating this guard
      # even if not all of the inputs are known.  For example, a 2D
      # tensor with non-0/1 sizes but strides (0, 1) is definitely
      # false, because we know its numel > 1 but it's broadcasted
      # in dim 0.
      if all(isinstance(a, sympy.Integer) for a in args):
          # sym_node imported in torch.__init__. Local import to avoid an import cycle
          from torch.fx.experimental.symbolic_shapes import (
              eval_is_non_overlapping_and_dense,
          )
  
          size_args = args[0:dim]
          stride_args = args[dim:]
          return eval_is_non_overlapping_and_dense(
              [int(a) for a in size_args], [int(a) for a in stride_args]
          )
      return None
  
imports: import sympy
  from sympy import S
  from sympy.core.logic import fuzzy_and, fuzzy_not, fuzzy_or
label_desc: Use image node_id 1 for calling the IsNonOverlappingAndDenseIndicator obj's underlying member method code with example usage: obj.eval(cls) and returns: None, eval_is_non_overlapping_and_dense