class_docstr: Create a uniform distribution object.
  
  Parameters
  ----------
  low : Tensor or scalar, default 0
      lower range of the distribution.
  high : Tensor or scalar, default 1
      upper range of the distribution.
class_docstr_tok: ["Create","a","uniform","distribution","object",".","Parameters","--","--","--","--","--","low",":","Tensor","or","scalar",",","default","0","lower","range","of","the","distribution",".","high",":","Tensor","or","scalar",",","default","1","upper","range","of","the","distribution","."]
code: def broadcast_to(self, batch_shape):
      new_instance = self.__new__(type(self))
      new_instance.low = np.broadcast_to(self.low, batch_shape)
      new_instance.high = np.broadcast_to(self.high, batch_shape)
      super(Uniform, new_instance).__init__(
          event_dim=self.event_dim, validate_args=False
      )
      new_instance._validate_args = self._validate_args
      return new_instance
  
file_path: mxnet/python/mxnet/gluon/probability/distributions/uniform.py
imports: from .distribution import Distribution
  from .constraint import Real, Interval
  from .utils import sample_n_shape_converter
  from ....None import np
label_desc: Use image node_id 6 for calling the Uniform obj's underlying member method code with example usage: obj.broadcast_to(batch_shape) and returns: new_instance