code: def _evaluate(chunk):
      op_type = type(chunk.op)
      if op_type in NE_UNARYOP_TO_STRING:
          return _handle_unary(chunk)
      elif op_type in NE_BINOP_TO_STRING:
          return _handle_bin(chunk)
      elif op_type in NE_TREE_OP_TO_STRING:
          return _handle_tree(chunk)
      elif op_type in NE_REDUCTION_TO_STRING:
          return _handle_reduction(chunk)
      elif op_type is TensorNeFuseChunk:
          return _decompose(chunk)
      else:
          raise TypeError(
              f"unsupported operator in numexpr: {op_type.__name__}"
          )
  
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 7 for calling a global function with example usage: _evaluate(chunk) and returns: _handle_unary, _handle_bin, _handle_tree, _handle_reduction, _decompose