code: def _basic_block(x, planes, stride=1, downsample=None, name=None):
      identity = x
  
      out = _conv3x3(x, planes, stride=stride, name=f"{name}.conv1")
      out = tf.keras.layers.BatchNormalization(
          momentum=0.9, epsilon=1e-5, name=f"{name}.bn1"
      )(out)
      out = tf.keras.layers.ReLU(name=f"{name}.relu1")(out)
  
      out = _conv3x3(out, planes, name=f"{name}.conv2")
      out = tf.keras.layers.BatchNormalization(
          momentum=0.9, epsilon=1e-5, name=f"{name}.bn2"
      )(out)
  
      if downsample is not None:
          for layer in downsample:
              identity = layer(identity)
  
      out = tf.keras.layers.Add(name=f"{name}.add")([identity, out])
      out = tf.keras.layers.ReLU(name=f"{name}.relu2")(out)
  
      return out
  
imports: from absl.testing import absltest
  from absl.testing import parameterized
  import jax
  from jax import numpy
  import neural_tangents
  from neural_tangents import experimental
  import numpy
  import tensorflow
label_desc: Use image node_id 10 for calling a global function with example usage: _basic_block(x, planes, stride, downsample, name) and returns: out