class_docstr: Sample initial weights from the Gaussian distribution.
  
  Initial weight parameters are sampled from N(mean, std).
  
  Parameters
  ----------
  std : float
      Std of initial parameters.
  mean : float
      Mean of initial parameters.
class_docstr_tok: ["Sample","initial","weights","from","the","Gaussian","distribution",".","Initial","weight","parameters","are","sampled","from","N","(","mean",",","std",")",".","Parameters","--","--","--","--","--","std",":","float","Std","of","initial","parameters",".","mean",":","float","Mean","of","initial","parameters","."]
code: def sample(self, shape):
      return floatX(get_rng().normal(self.mean, self.std, size=shape))
  
imports: import numpy
  from .utils import floatX
  from .random import get_rng
label_desc: Use image node_id 2 for calling the Normal obj's underlying member method code with example usage: obj.sample(shape) and returns: floatX