class_docstr: Generates uniformly distributed random samples from the half-open interval
  ``[low, high)``.
  
  Example::
  
      >>> m = Uniform(torch.tensor([0.0]), torch.tensor([5.0]))
      >>> m.sample()  # uniformly distributed in the range [0.0, 5.0)
      >>> # xdoctest: +SKIP
      tensor([ 2.3418])
  
  Args:
      low (float or Tensor): lower range (inclusive).
      high (float or Tensor): upper range (exclusive).
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code: def mode(self):
      return nan * self.high
  
file_path: pytorch/torch/distributions/uniform.py
imports: from numbers import Number
  import torch
  from torch import nan
  from torch.distributions import constraints
  from torch.distributions.distribution import Distribution
  from torch.distributions.utils import broadcast_all
label_desc: Use image node_id 2 for calling the Uniform obj's underlying member method code with example usage: obj.mode() and returns: unknown