code: def sampleUnif(x, n: int = 10000, seed: Optional[int] = None):
      """
      Generate samples from a uniform distribution over the max / min of each column of the sample X.
  
      These are used for estimation of support, as the number of samples included under the rules gives a measure of
      volume.  This function is specialized to continuous variables, while `sample_reference` handles the general case,
      calling this function where necessary.
  
      :param x: 2D array of samples, where each column corresponds to a feature.
      :type x: Pandas Dataframe or Numpy Array
      :param n: int, defaults to 10000
      :type n: int, optional
      :param seed: Random seed for uniform sampling, defaults to None
      :type seed: int, optional
      """
      rng = np.random.default_rng(seed)
  
      xMin, xMax = np.nanmin(x, axis=0), np.nanmax(x, axis=0)
      refSamples = rng.uniform(
          low=xMin.tolist(), high=xMax.tolist(), size=(n, xMin.shape[0])
      )
  
      assert refSamples.shape[1] == x.shape[1]
      return refSamples
  
imports: from typing import Dict, List, Optional, Union
  import numpy
  import pandas
label_desc: Use image node_id 1 for calling a global function with example usage: sampleUnif(x, n, seed) and returns: refSamples