code: def renormalize(tensor, has_batch=False):
      shape = tensor.shape
      if not has_batch:
          tensor = jnp.expand_dims(tensor, 0)
      tensor = tensor.reshape(tensor.shape[0], -1)
      max_val = jnp.max(tensor, axis=-1, keepdims=True)
      min_val = jnp.min(tensor, axis=-1, keepdims=True)
      return ((tensor - min_val) / (max_val - min_val + 1e-5)).reshape(
          *shape
      )
  
imports: import collections
  import functools
  import time
  from typing import Any, Callable, Optional, Tuple
  from dopamine.jax.networks import preprocess_atari_inputs
  from flax import linen
  import gin
  import jax
  from jax import lax
  from jax import random
  from jax.nn import initializers
  import jax.numpy
  import numpy
label_desc: Use image node_id 3 for calling a global function with example usage: renormalize(tensor, has_batch) and returns: unknown