code: def execute(cls, ctx, op):
      chunk = op.outputs[0]
      inputs = as_same_device(
          [ctx[c.key] for c in op.inputs], device=op.device
      )
      counter = count()
      # Unified the var names to V_0, V_1, ... for better cache hit.
      key_to_var = defaultdict(lambda: f"V_{counter.__next__()}")
      local_dict = {
          key_to_var[c.key]: i for c, i in zip(op.inputs, inputs)
      }
      expr = _evaluate(chunk).format_map(key_to_var)
      # The numexpr.evaluate is thread safe: https://github.com/pydata/numexpr/pull/200
      try:
          res = ne.evaluate(expr, local_dict=local_dict, global_dict={})
      except Exception as e:
          raise RuntimeError(
              f"Failed to evaluate numexpr {repr(expr)} on local dict {local_dict}."
          ) from e
      res = _maybe_keepdims(chunk, res)
      if chunk.ndim == 0 and res.ndim == 1 and res.size == 0:
          res = res.dtype.type(0)
      ctx[chunk.key] = res
  
imports: from collections import defaultdict
  from itertools import count
  import numpy
  from ..operands import TensorFuse
  from ..None import arithmetic, reduction
  from ..array_utils import as_same_device
  from .core import TensorFuseChunkMixin
label_desc: Use image node_id 1 for calling the TensorNeFuseChunk obj's underlying member method code with example usage: obj.execute(cls, ctx, op) without return types