class_docstr: Learnable weights as a layer.
  
  It takes no input and returns a single tensor: weights.
class_docstr_tok: ["Learnable","weights","as","a","layer",".","It","takes","no","input","and","returns","a","single","tensor",":","weights","."]
docstr: Returns newly initialized weights for this layer.
  
  Weights is a single  `w` tensor with previously specified shape.
  
  Args:
    input_signature: `ShapeDtype` instance characterizing the input this layer
        should compute on. Unused.
docstr_tok: ["Returns","newly","initialized","weights","for","this","layer",".","Weights","is","a","single","`","w","`","tensor","with","previously","specified","shape",".","Args",":","input_signature",":","`","ShapeDtype","`","instance","characterizing","the","input","this","layer","should","compute","on",".","Unused","."]
code: def init_weights_and_state(self, input_signature):
      """Returns newly initialized weights for this layer.
  
      Weights is a single  `w` tensor with previously specified shape.
  
      Args:
        input_signature: `ShapeDtype` instance characterizing the input this layer
            should compute on. Unused.
      """
      del input_signature  # Unused. There is no input to this layer.
      self.weights = self._initializer(self._shape, self.rng)
      if self._use_bfloat16:
          self.weights = self.weights.astype(jnp.bfloat16)
  
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 3 for calling the Weights obj's underlying member method code with example usage: obj.init_weights_and_state(input_signature) without return types