class_docstr: Draw random samples from time series.
  
  `transform` takes a time series :math:`X={x_1, x_2, ... , x_m}` with :math:`m`
  elements and returns :math:`X_t={x_i, x_{i+1}, ... , x_n}`, where
  :math:`{x_i, x_{i+1}, ... , x_n}` are :math:`n`=``n`` random samples drawn
  from :math:`X` (with or `without_replacement`).
  
  Parameters
  ----------
  n: int or float, optional (default = 1.0)
          To specify an exact number of samples to draw, set `n` to an int value.
          Number of samples to draw.
          To specify the returned samples as a proportion of the given times series
          set `n` to a float value :math:`n \in [0, 1]`.
          By default, the same number of samples is returned as given by the input
          time series.
  without_replacement: bool, optional (default = True)
          Whether to draw without replacement. If True, every sample of the input
          times series `X` will appear at most once in `Xt`.
  random_state: None or int or ``np.random.RandomState`` instance, optional
          "If int or RandomState, use it for drawing the random variates.
          If None, rely on ``self.random_state``.
          Default is None." [1]
  
  References and Footnotes
  ----------
  
      [1]: https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.rv_continuous.random_state.html # noqa
code: def _transform(self, X, y=None):
      if isinstance(self.n, float):
          n = int(np.ceil(self.n * len(X)))
      else:
          n = self.n
      rng = check_random_state(self.random_state)
      values = np.concatenate(X.values)
      if self.without_replacement:
          replace = False
      else:
          replace = True
      Xt = rng.choice(values, n, replace)
      return pd.DataFrame(Xt)
  
imports: import numpy
  import pandas
  from sklearn.utils import check_random_state
  from sktime.transformations.base import BaseTransformer
label_desc: Use image node_id 2 for calling the RandomSamplesAugmenter obj's underlying member method code with example usage: obj._transform(X, y) and returns: pd