code: def query_compile(expr):
      """Compile the query expression.
  
      This generates a CUDA Kernel for the query expression.  The kernel is
      cached for reuse.  All variable names, including both references to
      columns and references to variables in the calling environment, in the
      expression are passed as argument to the kernel. Thus, the kernel is
      reusable on any dataframe and in any environment.
  
      Parameters
      ----------
      expr : str
          The boolean expression
  
      Returns
      -------
      compiled: dict
          key "kernel" is the cuda kernel for the query.
          key "args" is a sequence of name of the arguments.
      """
  
      # hash returns in the semi-open interval [-2**63, 2**63)
      funcid = f"queryexpr_{(hash(expr) + 2**63):x}"
      # Load cache
      compiled = _cache.get(funcid)
      # Cache not found
      if compiled is None:
          info = query_parser(expr)
          fn = query_builder(info, funcid)
          args = info["args"]
          # compile
          devicefn = cuda.jit(device=True)(fn)
  
          kernelid = f"kernel_{funcid}"
          kernel = _wrap_query_expr(kernelid, devicefn, args)
  
          compiled = info.copy()
          compiled["kernel"] = kernel
          # Store cache
          _cache[funcid] = compiled
      return compiled
  
imports: import ast
  import datetime
  from typing import Any, Dict
  import numpy
  from numba import cuda
  import cudf
  from cudf.core.buffer import acquire_spill_lock
  from cudf.core.column import column_empty
  from cudf.utils import applyutils
  from cudf.utils._numba import _CUDFNumbaConfig
  from cudf.utils.dtypes import BOOL_TYPES, DATETIME_TYPES, NUMERIC_TYPES, TIMEDELTA_TYPES
label_desc: Use image node_id 4 for calling a global function with example usage: query_compile(expr) and returns: compiled