code: def coerce_to_array(values, dtype, mask=None, copy=False):
      """
      Coerce the input values array to numpy arrays with a mask
  
      Parameters
      ----------
      values : 1D list-like
      dtype : integer dtype
      mask : boolean 1D array, optional
      copy : boolean, default False
          if True, copy the input
  
      Returns
      -------
      tuple of (values, mask)
      """
      # if values is integer numpy array, preserve it's dtype
      if dtype is None and hasattr(values, "dtype"):
          if is_integer_dtype(values.dtype):
              dtype = values.dtype
  
      if dtype is not None:
          if isinstance(dtype, string_types) and (
              dtype.startswith("Int") or dtype.startswith("UInt")
          ):
              # Avoid DeprecationWarning from NumPy about np.dtype("Int64")
              # https://github.com/numpy/numpy/pull/7476
              dtype = dtype.lower()
  
          if not issubclass(type(dtype), _IntegerDtype):
              try:
                  dtype = _dtypes[str(np.dtype(dtype))]
              except KeyError:
                  raise ValueError(
                      "invalid dtype specified {}".format(dtype)
                  )
  
      if isinstance(values, IntegerArray):
          values, mask = values._data, values._mask
          if dtype is not None:
              values = values.astype(dtype.numpy_dtype, copy=False)
  
          if copy:
              values = values.copy()
              mask = mask.copy()
          return values, mask
  
      values = np.array(values, copy=copy)
      if is_object_dtype(values):
          inferred_type = lib.infer_dtype(values, skipna=True)
          if inferred_type == "empty":
              values = np.empty(len(values))
              values.fill(np.nan)
          elif inferred_type not in [
              "floating",
              "integer",
              "mixed-integer",
              "mixed-integer-float",
          ]:
              raise TypeError(
                  "{} cannot be converted to an IntegerDtype".format(
                      values.dtype
                  )
              )
  
      elif not (is_integer_dtype(values) or is_float_dtype(values)):
          raise TypeError(
              "{} cannot be converted to an IntegerDtype".format(
                  values.dtype
              )
          )
  
      if mask is None:
          mask = isna(values)
      else:
          assert len(mask) == len(values)
  
      if not values.ndim == 1:
          raise TypeError("values must be a 1D list-like")
      if not mask.ndim == 1:
          raise TypeError("mask must be a 1D list-like")
  
      # infer dtype if needed
      if dtype is None:
          dtype = np.dtype("int64")
      else:
          dtype = dtype.type
  
      # if we are float, let's make sure that we can
      # safely cast
  
      # we copy as need to coerce here
      if mask.any():
          values = values.copy()
          values[mask] = 1
          values = safe_cast(values, dtype, copy=False)
      else:
          values = safe_cast(values, dtype, copy=False)
  
      return values, mask
  
imports: import copy
  import sys
  import warnings
  import numpy
  from pandas._libs import lib
  from pandas.compat import range, set_function_name, string_types
  from pandas.util._decorators import cache_readonly
  from pandas.core.dtypes.base import ExtensionDtype
  from pandas.core.dtypes.cast import astype_nansafe
  from pandas.core.dtypes.common import is_bool_dtype, is_float, is_float_dtype, is_integer, is_integer_dtype, is_list_like, is_object_dtype, is_scalar
  from pandas.core.dtypes.dtypes import register_extension_dtype
  from pandas.core.dtypes.generic import ABCIndexClass, ABCSeries
  from pandas.core.dtypes.missing import isna, notna
  from pandas.core import nanops
  from pandas.core.arrays import ExtensionArray, ExtensionOpsMixin
  from pandas.core.tools.numeric import to_numeric
label_desc: Use image node_id 3 for calling a global function with example usage: coerce_to_array(values, dtype, mask, copy) and returns: values,  mask, values,  mask