code: def get_dropout_mask(
      self, shape: Shape, drop: Optional[float]
  ) -> FloatsXd:
      if drop is None or drop <= 0:
          return self.xp.ones(shape, dtype="f")
      else:
          raise ValueError(
              "During prediction, dropout should not be applied"
          )
  
imports: from typing import List, Optional
  import numpy
  import pytest
  import srsly
  from numpy.testing import assert_almost_equal
  from thinc.api import Dropout, Model, NumpyOps, registry, with_padded
  from thinc.backends import NumpyOps
  from thinc.compat import has_torch
  from thinc.types import Array2d, Floats2d, FloatsXd, Padded, Ragged, Shape
  from thinc.util import data_validation, get_width
label_desc: Use image node_id 1 for calling the NoDropoutOps obj's underlying member method code with example usage: obj.get_dropout_mask(shape, drop) and returns: self