class_docstr: Create a FisherSnedecor distribution object, often known as F distribution.
  
  Parameters
  ----------
  df1 : Tensor or scalar
      degree of freedom parameter 1
  scale : Tensor or scalar
      degree of freedom parameter 2
class_docstr_tok: ["Create","a","FisherSnedecor","distribution","object",",","often","known","as","F","distribution",".","Parameters","--","--","--","--","--","df1",":","Tensor","or","scalar","degree","of","freedom","parameter","1","scale",":","Tensor","or","scalar","degree","of","freedom","parameter","2"]
code: def broadcast_to(self, batch_shape):
      new_instance = self.__new__(type(self))
      new_instance.df1 = np.broadcast_to(self.df1, batch_shape)
      new_instance.df2 = np.broadcast_to(self.df2, batch_shape)
      new_instance._gamma1 = self._gamma1.broadcast_to(batch_shape)
      new_instance._gamma2 = self._gamma2.broadcast_to(batch_shape)
      super(FisherSnedecor, new_instance).__init__(
          event_dim=0, validate_args=False
      )
      new_instance._validate_args = self._validate_args
      return new_instance
  
file_path: mxnet/python/mxnet/gluon/probability/distributions/fishersnedecor.py
imports: from numpy import nan
  from .distribution import Distribution
  from .gamma import Gamma
  from .constraint import Positive
  from .utils import gammaln
  from ....None import np
label_desc: Use image node_id 2 for calling the FisherSnedecor obj's underlying member method code with example usage: obj.broadcast_to(batch_shape) and returns: new_instance