code: def coerce_to_array(
      values, mask=None, copy: bool = False
  ) -> tuple[np.ndarray, np.ndarray]:
      """
      Coerce the input values array to numpy arrays with a mask.
  
      Parameters
      ----------
      values : 1D list-like
      mask : bool 1D array, optional
      copy : bool, default False
          if True, copy the input
  
      Returns
      -------
      tuple of (values, mask)
      """
      if isinstance(values, BooleanArray):
          if mask is not None:
              raise ValueError(
                  "cannot pass mask for BooleanArray input"
              )
          values, mask = values._data, values._mask
          if copy:
              values = values.copy()
              mask = mask.copy()
          return values, mask
  
      mask_values = None
      if isinstance(values, np.ndarray) and values.dtype == np.bool_:
          if copy:
              values = values.copy()
      elif isinstance(values, np.ndarray) and is_numeric_dtype(
          values.dtype
      ):
          mask_values = isna(values)
  
          values_bool = np.zeros(len(values), dtype=bool)
          values_bool[~mask_values] = values[~mask_values].astype(bool)
  
          if not np.all(
              values_bool[~mask_values].astype(values.dtype)
              == values[~mask_values]
          ):
              raise TypeError("Need to pass bool-like values")
  
          values = values_bool
      else:
          values_object = np.asarray(values, dtype=object)
  
          inferred_dtype = lib.infer_dtype(values_object, skipna=True)
          integer_like = ("floating", "integer", "mixed-integer-float")
          if inferred_dtype not in ("boolean", "empty") + integer_like:
              raise TypeError("Need to pass bool-like values")
  
          # mypy does not narrow the type of mask_values to npt.NDArray[np.bool_]
          # within this branch, it assumes it can also be None
          mask_values = cast(
              "npt.NDArray[np.bool_]", isna(values_object)
          )
          values = np.zeros(len(values), dtype=bool)
          values[~mask_values] = values_object[~mask_values].astype(
              bool
          )
  
          # if the values were integer-like, validate it were actually 0/1's
          if (inferred_dtype in integer_like) and not (
              np.all(
                  values[~mask_values].astype(float)
                  == values_object[~mask_values].astype(float)
              )
          ):
              raise TypeError("Need to pass bool-like values")
  
      if mask is None and mask_values is None:
          mask = np.zeros(values.shape, dtype=bool)
      elif mask is None:
          mask = mask_values
      else:
          if isinstance(mask, np.ndarray) and mask.dtype == np.bool_:
              if mask_values is not None:
                  mask = mask | mask_values
              else:
                  if copy:
                      mask = mask.copy()
          else:
              mask = np.array(mask, dtype=bool)
              if mask_values is not None:
                  mask = mask | mask_values
  
      if values.shape != mask.shape:
          raise ValueError("values.shape and mask.shape must match")
  
      return values, mask
  
imports: from __future__ import annotations
  import numbers
  from typing import TYPE_CHECKING, cast
  import numpy
  from pandas._libs import lib, missing
  from pandas._typing import Dtype, DtypeObj, type_t
  from pandas.core.dtypes.common import is_list_like, is_numeric_dtype
  from pandas.core.dtypes.dtypes import register_extension_dtype
  from pandas.core.dtypes.missing import isna
  from pandas.core import ops
  from pandas.core.array_algos import masked_accumulations
  from pandas.core.arrays.masked import BaseMaskedArray, BaseMaskedDtype
label_desc: Use image node_id 1 for calling a global function with example usage: coerce_to_array(values, mask, copy) and returns: values,  mask, values,  mask