!5186 Add note of limitation for prarmeters of uniform

Merge pull request !5186 from peixu_ren/custom_pp_ops

mindspore/nn/probability/README.md

@@ -32,7 +32,7 @@ The objective of MDP is to integrate deep learning with Bayesian learning. On th
 - Context: context managers for models and layers.
 
  **Layer 3: Toolbox provides a set of BNN tools for some specific applications** 
-- Uncertainty Estimation([mindspore.nn.probability.toolbox.uncertainty_evaluate](https://gitee.com/mindspore/mindspore/tree/master/mindspore/nn/probability/toolbox/uncertainty_evaluate.py)): Interfaces to estimate epistemic uncertainty and aleatoric uncertainty.
+- Uncertainty Estimation([mindspore.nn.probability.toolbox.uncertainty_evaluation](https://gitee.com/mindspore/mindspore/tree/master/mindspore/nn/probability/toolbox/uncertainty_evaluation.py)): Interfaces to estimate epistemic uncertainty and aleatoric uncertainty.
 - OoD detection: Interfaces to detect out of distribution samples.
 
 ![Structure of MDP](https://images.gitee.com/uploads/images/2020/0820/115117_2a20da64_7825995.png "MDP.png")

mindspore/ops/composite/random_ops.py

@@ -96,6 +96,9 @@ def uniform(shape, a, b, seed=0, dtype=mstype.float32):
     """
     Generates random numbers according to the Uniform random number distribution.
 
+    Note:
+        The number in tensor a should be strictly less than b at any position after broadcasting.
+
     Args:
         shape (tuple): The shape of random tensor to be generated.
         a (Tensor): The a distribution parameter.