class_docstr: Layer returning a tensor with random values distributed uniformly.
class_docstr_tok: ["Layer","returning","a","tensor","with","random","values","distributed","uniformly","."]
docstr: Executes this layer as part of a forward pass through the model.
  
  Args:
    xs: Unused tensors.
  
  Returns:
    Random uniform tensor of the shape and type specified in constructor.
docstr_tok: ["Executes","this","layer","as","part","of","a","forward","pass","through","the","model",".","Args",":","xs",":","Unused","tensors",".","Returns",":","Random","uniform","tensor","of","the","shape","and","type","specified","in","constructor","."]
code: def forward(self, xs):
      """Executes this layer as part of a forward pass through the model.
  
      Args:
        xs: Unused tensors.
  
      Returns:
        Random uniform tensor of the shape and type specified in constructor.
      """
      rng = self._get_conditionally_synced_rng()
      result = fastmath.random.uniform(
          rng, self._shape, self._dtype, self._min_val, self._max_val
      )
      return result
  
imports: from absl import logging
  import numpy
  import tensorflow
  from trax import fastmath
  from trax.fastmath import numpy
  from trax.layers import base
  from trax.layers import initializers
  from trax.layers.assert_shape import assert_shape
  from trax.layers.base import Fn
label_desc: Use image node_id 2 for calling the RandomUniform obj's underlying member method code with example usage: obj.forward(xs) and returns: result