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: Executes this layer as part of a forward pass through the model.
  
  Args:
    x: Tensor of same shape and dtype as the input signature used to
        initialize this layer.
  
  Returns:
    Tensor with previously specified shape and dtype.
docstr_tok: ["Executes","this","layer","as","part","of","a","forward","pass","through","the","model",".","Args",":","x",":","Tensor","of","same","shape","and","dtype","as","the","input","signature","used","to","initialize","this","layer",".","Returns",":","Tensor","with","previously","specified","shape","and","dtype","."]
code: def forward(self, x):
      """Executes this layer as part of a forward pass through the model.
  
      Args:
        x: Tensor of same shape and dtype as the input signature used to
            initialize this layer.
  
      Returns:
        Tensor with previously specified shape and dtype.
      """
      del x  # Unused. There is no input to this layer.
      return self.weights
  
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 Weights obj's underlying member method code with example usage: obj.forward(x) and returns: self