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 __init__(self, std=0.01, mean=0.0):
      self.std = std
      self.mean = mean
  
imports: import numpy
  from .utils import floatX
  from .random import get_rng
label_desc: Use image node_id 1 to create a new Normal object from inherited base classes: Initializer with example: obj = Normal(std, mean)