code: def ensure_int64_or_float64(arr, copy=False):
      """
      Ensure that an dtype array of some integer dtype
      has an int64 dtype if possible
      If it's not possible, potentially because of overflow,
      convert the array to float64 instead.
  
      Parameters
      ----------
      arr : array-like
            The array whose data type we want to enforce.
      copy: boolean
            Whether to copy the original array or reuse
            it in place, if possible.
  
      Returns
      -------
      out_arr : The input array cast as int64 if
                possible without overflow.
                Otherwise the input array cast to float64.
      """
      try:
          return arr.astype("int64", copy=copy, casting="safe")
      except TypeError:
          return arr.astype("float64", copy=copy)
  
imports: import warnings
  import numpy
  from pandas._libs import algos, lib
  from pandas._libs.tslibs import conversion
  from pandas.compat import PY3, PY36, string_types
  from pandas.core.dtypes.dtypes import CategoricalDtype, DatetimeTZDtype, ExtensionDtype, IntervalDtype, PandasExtensionDtype, PeriodDtype, registry
  from pandas.core.dtypes.generic import ABCCategorical, ABCDateOffset, ABCDatetimeIndex, ABCIndexClass, ABCPeriodArray, ABCPeriodIndex, ABCSeries
  from pandas.core.dtypes.inference import is_array_like, is_bool, is_complex, is_decimal, is_dict_like, is_file_like, is_float, is_hashable, is_integer, is_interval, is_iterator, is_list_like, is_named_tuple, is_nested_list_like, is_number, is_re, is_re_compilable, is_scalar, is_sequence, is_string_like
label_desc: Use image node_id 3 for calling a global function with example usage: ensure_int64_or_float64(arr, copy) and returns: arr, arr