code: def _fast_inference_init_state(
      input_signature, buffer_length, predict_mask=None
  ):
      """Returns an initial state for causal attention layer fast inference."""
  
      def zeros_for(batch_size, shape_dtype):
          shape, dtype = shape_dtype.as_tuple()
          d_feature = shape[-1]
          return jnp.zeros(
              (batch_size, buffer_length, d_feature), dtype=dtype
          )
  
      batch_size = input_signature[0].shape[0]
      k = zeros_for(batch_size, input_signature[1])
      v = zeros_for(batch_size, input_signature[2])
      if predict_mask is not None:
          mask_for_predict = jnp.zeros((buffer_length,)) != 0
          return (mask_for_predict, k, v, jnp.array(0))
      else:
          return (k, v, jnp.array(0))
  
imports: import numpy
  from trax import fastmath
  from trax.fastmath import numpy
  from trax.layers import base
  from trax.layers import combinators
  from trax.layers import convolution
  from trax.layers import core
  from trax.layers import initializers
  from trax.layers.assert_shape import assert_shape
  from trax.layers.base import Fn
  from trax.layers.research import sparsity
label_desc: Use image node_id 13 for calling a global function with example usage: _fast_inference_init_state(input_signature, buffer_length, predict_mask) and returns: jnp, mask_for_predict,  k,  v,  jnp, k,  v,  jnp