code: def infer_dtype_from_array(arr, pandas_dtype=False):
      """
      infer the dtype from a scalar or array
  
      Parameters
      ----------
      arr : scalar or array
      pandas_dtype : bool, default False
          whether to infer dtype including pandas extension types.
          If False, array belongs to pandas extension types
          is inferred as object
  
      Returns
      -------
      tuple (numpy-compat/pandas-compat dtype, array)
  
      Notes
      -----
      if pandas_dtype=False. these infer to numpy dtypes
      exactly with the exception that mixed / object dtypes
      are not coerced by stringifying or conversion
  
      if pandas_dtype=True. datetime64tz-aware/categorical
      types will retain there character.
  
      Examples
      --------
      >>> np.asarray([1, '1'])
      array(['1', '1'], dtype='<U21')
  
      >>> infer_dtype_from_array([1, '1'])
      (numpy.object_, [1, '1'])
  
      """
  
      if isinstance(arr, np.ndarray):
          return arr.dtype, arr
  
      if not is_list_like(arr):
          arr = [arr]
  
      if pandas_dtype and is_extension_type(arr):
          return arr.dtype, arr
  
      elif isinstance(arr, ABCSeries):
          return arr.dtype, np.asarray(arr)
  
      # don't force numpy coerce with nan's
      inferred = lib.infer_dtype(arr, skipna=False)
      if inferred in [
          "string",
          "bytes",
          "unicode",
          "mixed",
          "mixed-integer",
      ]:
          return (np.object_, arr)
  
      arr = np.asarray(arr)
      return arr.dtype, arr
  
imports: from datetime import datetime, timedelta
  import numpy
  from pandas._libs import lib, tslib, tslibs
  from pandas._libs.tslibs import NaT, OutOfBoundsDatetime, Period, iNaT
  from pandas.compat import PY3, string_types, text_type, to_str
  from .common import _INT64_DTYPE, _NS_DTYPE, _POSSIBLY_CAST_DTYPES, _TD_DTYPE, ensure_int8, ensure_int16, ensure_int32, ensure_int64, ensure_object, is_bool, is_bool_dtype, is_categorical_dtype, is_complex, is_complex_dtype, is_datetime64_dtype, is_datetime64_ns_dtype, is_datetime64tz_dtype, is_datetime_or_timedelta_dtype, is_datetimelike, is_dtype_equal, is_extension_array_dtype, is_extension_type, is_float, is_float_dtype, is_integer, is_integer_dtype, is_object_dtype, is_scalar, is_string_dtype, is_timedelta64_dtype, is_timedelta64_ns_dtype, is_unsigned_integer_dtype, pandas_dtype
  from .dtypes import DatetimeTZDtype, ExtensionDtype, PandasExtensionDtype, PeriodDtype
  from .generic import ABCDatetimeArray, ABCDatetimeIndex, ABCPeriodArray, ABCPeriodIndex, ABCSeries
  from .inference import is_list_like
  from .missing import isna, notna
label_desc: Use image node_id 8 for calling a global function with example usage: infer_dtype_from_array(arr, pandas_dtype) and returns: arr,  arr, arr,  arr, arr,  arr, np,  arr, arr,  np