class_docstr: ResNet model.
class_docstr_tok: ["ResNet","model","."]
code: def __call__(self, inputs, is_training, test_local_stats=False):
      out = inputs
      out = self.initial_conv(out)
      if not self.resnet_v2:
          out = self.initial_batchnorm(
              out, is_training, test_local_stats
          )
          out = jax.nn.relu(out)
  
      out = hk.max_pool(
          out,
          window_shape=(1, 3, 3, 1),
          strides=(1, 2, 2, 1),
          padding="SAME",
      )
  
      for block_group in self.block_groups:
          out = block_group(out, is_training, test_local_stats)
  
      if self.resnet_v2:
          out = self.final_batchnorm(out, is_training, test_local_stats)
          out = jax.nn.relu(out)
      out = jnp.mean(out, axis=(1, 2))
      return self.logits(out)
  
imports: from collections.abc import Mapping, Sequence
  from typing import Any, Optional, Union
  from haiku._src import basic
  from haiku._src import batch_norm
  from haiku._src import conv
  from haiku._src import module
  from haiku._src import pool
  import jax
  import jax.numpy
label_desc: Use image node_id 2 for calling the ResNet obj's underlying member method code with example usage: obj.__call__(inputs, is_training, test_local_stats) and returns: self