code: def maybe_cast_to_integer_array(arr, dtype, copy=False):
      
  
      try:
          if not hasattr(arr, "astype"):
              casted = np.array(arr, dtype=dtype, copy=copy)
          else:
              casted = arr.astype(dtype, copy=copy)
      except OverflowError:
          raise OverflowError("The elements provided in the data cannot all be "
                              "casted to the dtype {dtype}".format(dtype=dtype))
  
      if np.array_equal(arr, casted):
          return casted
  
      # We do this casting to allow for proper
      # data and dtype checking.
      #
      # We didn't do this earlier because NumPy
      # doesn't handle `uint64` correctly.
      arr = np.asarray(arr)
  
      if is_unsigned_integer_dtype(dtype) and (arr < 0).any():
          raise OverflowError("Trying to coerce negative values "
                              "to unsigned integers")
  
      if is_integer_dtype(dtype) and (is_float_dtype(arr) or
                                      is_object_dtype(arr)):
          raise ValueError("Trying to coerce float values to integers")
docs: Takes any dtype and returns the casted version, raising for when data is
      incompatible with integer/unsigned integer dtypes.
  
      .. versionadded:: 0.24.0
  
      Parameters
      ----------
      arr : array-like
          The array to cast.
      dtype : str, np.dtype
          The integer dtype to cast the array to.
      copy: boolean, default False
          Whether to make a copy of the array before returning.
  
      Returns
      -------
      int_arr : ndarray
          An array of integer or unsigned integer dtype
  
      Raises
      ------
      OverflowError : the dtype is incompatible with the data
      ValueError : loss of precision has occurred during casting
  
      Examples
      --------
      If you try to coerce negative values to unsigned integers, it raises:
  
      >>> Series([-1], dtype="uint64")
      Traceback (most recent call last):
          ...
      OverflowError: Trying to coerce negative values to unsigned integers
  
      Also, if you try to coerce float values to integers, it raises:
  
      >>> Series([1, 2, 3.5], dtype="int64")
      Traceback (most recent call last):
          ...
      ValueError: Trying to coerce float values to integers