code: def sparsity_ignore(m: str, n: str, v: jax.Array) -> bool:
      """Any parameter matching these conditions should generally not be pruned."""
      # n == 'b' when param is a bias
      return (
          n == "b"
          or v.ndim == 1
          or "batchnorm" in m
          or "batch_norm" in m
      )
  
imports: from collections.abc import Iterator, Mapping, Sequence
  import functools
  from typing import Callable
  from absl import app
  import haiku
  import jax
  import jax.numpy
  import numpy
  import optax
  import tensorflow_datasets
label_desc: Use image node_id 4 for calling a global function with example usage: sparsity_ignore(m, n, v) and returns: unknown