class_docstr: Uniform distribution.
  
  Parameters
  ----------
  low
      Tensor containing the lower bound of the distribution domain.
  high
      Tensor containing the higher bound of the distribution domain.
  F
class_docstr_tok: ["Uniform","distribution",".","Parameters","--","--","--","--","--","low","Tensor","containing","the","lower","bound","of","the","distribution","domain",".","high","Tensor","containing","the","higher","bound","of","the","distribution","domain",".","F"]
code: def sample_rep(
      self, num_samples: Optional[int] = None, dtype=np.float32
  ) -> Tensor:
      def s(low: Tensor, high: Tensor) -> Tensor:
          raw_samples = self.F.sample_uniform(
              low=low.zeros_like(), high=high.ones_like(), dtype=dtype
          )
          return low + raw_samples * (high - low)
  
      return _sample_multiple(
          s, low=self.low, high=self.high, num_samples=num_samples
      )
  
imports: from functools import partial
  from typing import Dict, List, Optional, Tuple
  import numpy
  from gluonts.core.component import validated
  from gluonts.mx import Tensor
  from .distribution import Distribution, _sample_multiple, getF, softplus
  from .distribution_output import DistributionOutput
label_desc: Use image node_id 10 for calling the Uniform obj's underlying member method code with example usage: obj.sample_rep(num_samples, dtype) and returns: _sample_multiple, unknown