class_docstr: Implements the sin-cos positional encoding.
class_docstr_tok: ["Implements","the","sin-cos","positional","encoding","."]
docstr: Randomly initializes the positional encoding vectors.
  
  Args:
    input_signature: `ShapeDtype` instance characterizing the input this
        layer should compute on.
docstr_tok: ["Randomly","initializes","the","positional","encoding","vectors",".","Args",":","input_signature",":","`","ShapeDtype","`","instance","characterizing","the","input","this","layer","should","compute","on","."]
code: def init_weights_and_state(self, input_signature):
      """Randomly initializes the positional encoding vectors.
  
      Args:
        input_signature: `ShapeDtype` instance characterizing the input this
            layer should compute on.
      """
      if self._mode == "predict":
          self.state = jnp.zeros((), dtype=jnp.int32)
  
imports: import logging
  import jax
  import jax.extend
  import numpy
  import trax
  from trax import fastmath
  from trax.fastmath import numpy
  from trax.layers import base
  from trax.layers import initializers
label_desc: Use image node_id 4 for calling the SinCosPositionalEncoding obj's underlying member method code with example usage: obj.init_weights_and_state(input_signature) without return types