class_docstr: Layer returning a tensor with random values distributed uniformly.
class_docstr_tok: ["Layer","returning","a","tensor","with","random","values","distributed","uniformly","."]
docstr: Layer returning a tensor with random values distributed uniformly.
  
  Args:
    min_val: Lower end of uniform distribution.
    max_val: Upper end of uniform distribution.
    shape: Shape of the tensor to return. Values are sampled independently.
    dtype: Type of value to return.
    sync: Whether to synchronise `rng` across devices.
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code: def __init__(
      self,
      min_val=0.0,
      max_val=1.0,
      shape=(),
      dtype=jnp.float32,
      sync=False,
  ):
      """Layer returning a tensor with random values distributed uniformly.
  
      Args:
        min_val: Lower end of uniform distribution.
        max_val: Upper end of uniform distribution.
        shape: Shape of the tensor to return. Values are sampled independently.
        dtype: Type of value to return.
        sync: Whether to synchronise `rng` across devices.
      """
      super().__init__(n_in=0, n_out=1)
      self._min_val = min_val
      self._max_val = max_val
      self._shape = shape
      self._dtype = dtype
      self._sync = sync
  
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 1 to create a new RandomUniform object from inherited base classes: base with example: obj = RandomUniform(min_val, max_val, shape, dtype, sync)