docstr: Check that we are all-NA of a type/dtype that is compatible with this dtype.
  Augments `self.is_na` with an additional check of the type of NA values.
docstr_tok: ["Check","that","we","are","all-NA","of","a","type\/dtype","that","is","compatible","with","this","dtype",".","Augments","`","self.is_na","`","with","an","additional","check","of","the","type","of","NA","values","."]
code: def _is_valid_na_for(self, dtype: DtypeObj) -> bool:
      """
      Check that we are all-NA of a type/dtype that is compatible with this dtype.
      Augments `self.is_na` with an additional check of the type of NA values.
      """
      if not self.is_na:
          return False
  
      blk = self.block
      if blk.dtype.kind == "V":
          return True
  
      if blk.dtype == object:
          values = blk.values
          return all(
              is_valid_na_for_dtype(x, dtype)
              for x in values.ravel(order="K")
          )
  
      na_value = blk.fill_value
      if na_value is NaT and blk.dtype != dtype:
          # e.g. we are dt64 and other is td64
          # fill_values match but we should not cast blk.values to dtype
          # TODO: this will need updating if we ever have non-nano dt64/td64
          return False
  
      if na_value is NA and needs_i8_conversion(dtype):
          # FIXME: kludge; test_append_empty_frame_with_timedelta64ns_nat
          #  e.g. blk.dtype == "Int64" and dtype is td64, we dont want
          #  to consider these as matching
          return False
  
      # TODO: better to use can_hold_element?
      return is_valid_na_for_dtype(na_value, dtype)
  
imports: from __future__ import annotations
  from typing import TYPE_CHECKING, cast
  import warnings
  import numpy
  from pandas._libs import NaT, algos, internals, lib
  from pandas._libs.missing import NA
  from pandas.util._decorators import cache_readonly
  from pandas.util._exceptions import find_stack_level
  from pandas.core.dtypes.cast import ensure_dtype_can_hold_na, find_common_type
  from pandas.core.dtypes.common import is_1d_only_ea_dtype, is_scalar, needs_i8_conversion
  from pandas.core.dtypes.concat import concat_compat
  from pandas.core.dtypes.dtypes import ExtensionDtype, SparseDtype
  from pandas.core.dtypes.missing import is_valid_na_for_dtype, isna, isna_all
  from pandas.core.construction import ensure_wrapped_if_datetimelike
  from pandas.core.internals.array_manager import ArrayManager
  from pandas.core.internals.blocks import ensure_block_shape, new_block_2d
  from pandas.core.internals.managers import BlockManager, make_na_array
label_desc: Use image node_id 3 for calling the JoinUnit obj's underlying member method code with example usage: obj._is_valid_na_for(dtype) and returns: is_valid_na_for_dtype, False, True, all, False, False