class_docstr: Exponential Distribution.
  
  The probability density function of the distribution is expressed as
  
  .. math::
      p(x;\lambda) = \lambda e^{-\lambda x}
  
  Args:
      lam(:class:`~chainer.Variable` or :ref:`ndarray`): Parameter of
          distribution :math:`\lambda`.
class_docstr_tok: ["Exponential","Distribution",".","The","probability","density","function","of","the","distribution","is","expressed","as","..","math",":",":","p","(","x",";","\\lambda",")","=","\\lambda","e^","{","-\\lambda","x","}","Args",":","lam","(",":","class",":","`","~chainer.Variable","`","or",":","ref",":","`","ndarray","`",")",":","Parameter","of","distribution",":","math",":","`","\\lambda","`","."]
code: def batch_shape(self):
      return self.lam.shape
  
imports: import chainer
  from chainer.backends import cuda
  from chainer import distribution
  from chainer.functions.array import where
  from chainer.functions.math import exponential
  from chainer.functions.math import exponential_m1
  from chainer.functions.math import logarithm_1p
  from chainer.utils import cache
label_desc: Use image node_id 4 for calling the Exponential obj's underlying member method code with example usage: obj.batch_shape() and returns: self