code: def _make_layer(x, planes, blocks, stride=1, name=None):
      downsample = None
      inplanes = x.shape[3]
      if stride != 1 or inplanes != planes:
          downsample = [
              tf.keras.layers.Conv2D(
                  filters=planes,
                  kernel_size=1,
                  strides=stride,
                  use_bias=False,
                  kernel_initializer=_kaiming_normal,
                  name=f"{name}.0.downsample.0",
              ),
              tf.keras.layers.BatchNormalization(
                  momentum=0.9,
                  epsilon=1e-5,
                  name=f"{name}.0.downsample.1",
              ),
          ]
  
      x = _basic_block(x, planes, stride, downsample, name=f"{name}.0")
      for i in range(1, blocks):
          x = _basic_block(x, planes, name=f"{name}.{i}")
  
      return x
  
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 11 for calling a global function with example usage: _make_layer(x, planes, blocks, stride, name) and returns: x