code: def np_can_hold_element(dtype: np.dtype, element: Any) -> Any:
      """
      Raise if we cannot losslessly set this element into an ndarray with this dtype.
  
      Specifically about places where we disagree with numpy.  i.e. there are
      cases where numpy will raise in doing the setitem that we do not check
      for here, e.g. setting str "X" into a numeric ndarray.
  
      Returns
      -------
      Any
          The element, potentially cast to the dtype.
  
      Raises
      ------
      ValueError : If we cannot losslessly store this element with this dtype.
      """
      if dtype == _dtype_obj:
          return element
  
      tipo = _maybe_infer_dtype_type(element)
  
      if dtype.kind in ["i", "u"]:
          if isinstance(element, range):
              if _dtype_can_hold_range(element, dtype):
                  return element
              raise LossySetitemError
  
          if is_integer(element) or (
              is_float(element) and element.is_integer()
          ):
              # e.g. test_setitem_series_int8 if we have a python int 1
              #  tipo may be np.int32, despite the fact that it will fit
              #  in smaller int dtypes.
              info = np.iinfo(dtype)
              if info.min <= element <= info.max:
                  return dtype.type(element)
              raise LossySetitemError
  
          if tipo is not None:
              if tipo.kind not in ["i", "u"]:
                  if (
                      isinstance(element, np.ndarray)
                      and element.dtype.kind == "f"
                  ):
                      # If all can be losslessly cast to integers, then we can hold them
                      with np.errstate(invalid="ignore"):
                          # We check afterwards if cast was losslessly, so no need to show
                          # the warning
                          casted = element.astype(dtype)
                      comp = casted == element
                      if comp.all():
                          # Return the casted values bc they can be passed to
                          #  np.putmask, whereas the raw values cannot.
                          #  see TestSetitemFloatNDarrayIntoIntegerSeries
                          return casted
                      raise LossySetitemError
  
                  # Anything other than integer we cannot hold
                  raise LossySetitemError
              if (
                  dtype.kind == "u"
                  and isinstance(element, np.ndarray)
                  and element.dtype.kind == "i"
              ):
                  # see test_where_uint64
                  casted = element.astype(dtype)
                  if (casted == element).all():
                      # TODO: faster to check (element >=0).all()?  potential
                      #  itemsize issues there?
                      return casted
                  raise LossySetitemError
              if dtype.itemsize < tipo.itemsize:
                  raise LossySetitemError
              if not isinstance(tipo, np.dtype):
                  # i.e. nullable IntegerDtype; we can put this into an ndarray
                  #  losslessly iff it has no NAs
                  if element._hasna:
                      raise LossySetitemError
                  return element
  
              return element
  
          raise LossySetitemError
  
      if dtype.kind == "f":
          if lib.is_integer(element) or lib.is_float(element):
              casted = dtype.type(element)
              if np.isnan(casted) or casted == element:
                  return casted
              # otherwise e.g. overflow see TestCoercionFloat32
              raise LossySetitemError
  
          if tipo is not None:
              # TODO: itemsize check?
              if tipo.kind not in ["f", "i", "u"]:
                  # Anything other than float/integer we cannot hold
                  raise LossySetitemError
              if not isinstance(tipo, np.dtype):
                  # i.e. nullable IntegerDtype or FloatingDtype;
                  #  we can put this into an ndarray losslessly iff it has no NAs
                  if element._hasna:
                      raise LossySetitemError
                  return element
              elif (
                  tipo.itemsize > dtype.itemsize
                  or tipo.kind != dtype.kind
              ):
                  if isinstance(element, np.ndarray):
                      # e.g. TestDataFrameIndexingWhere::test_where_alignment
                      casted = element.astype(dtype)
                      if np.array_equal(
                          casted, element, equal_nan=True
                      ):
                          return casted
                      raise LossySetitemError
  
              return element
  
          raise LossySetitemError
  
      if dtype.kind == "c":
          if (
              lib.is_integer(element)
              or lib.is_complex(element)
              or lib.is_float(element)
          ):
              if np.isnan(element):
                  # see test_where_complex GH#6345
                  return dtype.type(element)
  
              with warnings.catch_warnings():
                  warnings.filterwarnings("ignore")
                  casted = dtype.type(element)
              if casted == element:
                  return casted
              # otherwise e.g. overflow see test_32878_complex_itemsize
              raise LossySetitemError
  
          if tipo is not None:
              if tipo.kind in ["c", "f", "i", "u"]:
                  return element
              raise LossySetitemError
          raise LossySetitemError
  
      if dtype.kind == "b":
          if tipo is not None:
              if tipo.kind == "b":
                  if not isinstance(tipo, np.dtype):
                      # i.e. we have a BooleanArray
                      if element._hasna:
                          # i.e. there are pd.NA elements
                          raise LossySetitemError
                  return element
              raise LossySetitemError
          if lib.is_bool(element):
              return element
          raise LossySetitemError
  
      if dtype.kind == "S":
          # TODO: test tests.frame.methods.test_replace tests get here,
          #  need more targeted tests.  xref phofl has a PR about this
          if tipo is not None:
              if tipo.kind == "S" and tipo.itemsize <= dtype.itemsize:
                  return element
              raise LossySetitemError
          if (
              isinstance(element, bytes)
              and len(element) <= dtype.itemsize
          ):
              return element
          raise LossySetitemError
  
      if dtype.kind == "V":
          # i.e. np.void, which cannot hold _anything_
          raise LossySetitemError
  
      raise NotImplementedError(dtype)
  
imports: from __future__ import annotations
  import datetime
  import functools
  from typing import TYPE_CHECKING, Any, Literal, Sized, TypeVar, cast, overload
  import warnings
  import numpy
  from pandas._libs import lib
  from pandas._libs.missing import NA, NAType, checknull
  from pandas._libs.tslibs import NaT, OutOfBoundsDatetime, OutOfBoundsTimedelta, Timedelta, Timestamp, get_unit_from_dtype, is_supported_unit
  from pandas._libs.tslibs.timedeltas import array_to_timedelta64
  from pandas._typing import ArrayLike, Dtype, DtypeObj, NumpyIndexT, Scalar, npt
  from pandas.errors import IntCastingNaNError, LossySetitemError
  from pandas.core.dtypes.common import ensure_int8, ensure_int16, ensure_int32, ensure_int64, ensure_object, ensure_str, is_bool, is_bool_dtype, is_complex, is_complex_dtype, is_datetime64_dtype, is_extension_array_dtype, is_float, is_float_dtype, is_integer, is_integer_dtype, is_numeric_dtype, is_object_dtype, is_scalar, is_signed_integer_dtype, is_string_dtype, is_timedelta64_dtype, is_unsigned_integer_dtype, pandas_dtype
  from pandas.core.dtypes.dtypes import BaseMaskedDtype, CategoricalDtype, DatetimeTZDtype, ExtensionDtype, IntervalDtype, PandasExtensionDtype, PeriodDtype
  from pandas.core.dtypes.generic import ABCExtensionArray, ABCIndex, ABCSeries
  from pandas.core.dtypes.inference import is_list_like
  from pandas.core.dtypes.missing import is_valid_na_for_dtype, isna, na_value_for_dtype, notna
  from pandas.io._util import _arrow_dtype_mapping
label_desc: Use image node_id 47 for calling a global function with example usage: np_can_hold_element(dtype, element) and returns: element, element, element, element, element, element, dtype, element, casted, element, dtype, casted, element, element, element, casted, casted, casted