class_docstr: Filter Response Normalization layer without Threshold Linear Unit.
  
  c.f. https://arxiv.org/pdf/1911.09737.pdf
class_docstr_tok: ["Filter","Response","Normalization","layer","without","Threshold","Linear","Unit",".","c.f",".","https",":","\/\/arxiv.org\/pdf\/1911.09737.pdf"]
code: def init_weights_and_state(self, input_signature):
      # Usually (B, W, H, C)
      shape = input_signature.shape
      num_channels = shape[-1]
  
      gamma = jnp.ones((num_channels,), dtype=jnp.float32)
      beta = jnp.zeros((num_channels,), dtype=jnp.float32)
  
      epsilon_l = base.EMPTY_WEIGHTS
      if self._learn_epsilon:
          epsilon_l = (self._init_learnt_epsilon,)
  
      self.weights = gamma, beta, epsilon_l
  
imports: from trax.fastmath import numpy
  from trax.layers import base
label_desc: Use image node_id 3 for calling the FilterResponseNorm obj's underlying member method code with example usage: obj.init_weights_and_state(input_signature) without return types