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  import numpy as np
  from onnx import TensorProto
  from onnx import helper as oh
  
  from finn.transformation.base import Transformation
  from finn.transformation.infer_shapes import InferShapes
  from finn.util.basic import get_by_name
  
  
  class BatchNormToAffine(Transformation):
      """Replaces any test-time BatchNorm layers with Mul-Add layers."""
  
      def apply(self, model):
          graph = model.graph
          node_ind = 0
          graph_modified = False
          for n in graph.node:
              node_ind += 1
              if n.op_type == "BatchNormalization":
                  graph_modified = True
                  bn_input = n.input[0]
                  bn_output = n.output[0]
                  # extract batchnorm parameters as numpy arrays
                  scale = model.get_initializer(n.input[1])
                  bias = model.get_initializer(n.input[2])
                  mean = model.get_initializer(n.input[3])
                  variance = model.get_initializer(n.input[4])
                  epsilon = get_by_name(n.attribute, "epsilon")
                  epsilon = getattr(epsilon, "f", 1e-5)
                  # find A and B to compute batchnorm as affine transpose Ax+B
                  # TODO is a division by moving avg factor needed for variance?
                  A = scale / np.sqrt(epsilon + variance)
                  B = bias - (A * mean)
                  # see if we have surrounding Unsqueeze/Squeeze nodes we can remove
                  producer = model.find_producer(bn_input)
                  if producer is not None:
                      if producer.op_type == "Unsqueeze":
                          bn_input = producer.input[0]
                  consumer = model.find_consumer(bn_output)
                  if consumer is not None:
                      if consumer.op_type == "Squeeze":
                          bn_output = consumer.output[0]
                  data_shape = model.get_tensor_shape(bn_input)
                  assert A.ndim == B.ndim, "Unexpected mul/add dims in BatchNormToAffine"
                  assert (
                      len(data_shape) >= A.ndim
                  ), "Unexpected number of dims found in BatchNormToAffine"
                  # reshape the mul/add constants to match the data shape/dims
                  # by adding (1,) dimensions to the right
                  n_spatial_dims = len(data_shape) - 2
                  target_shape = (1, -1) + tuple(1 for i in range(n_spatial_dims))
                  A = A.reshape(target_shape)
                  B = B.reshape(target_shape)
                  # create value_info and initializers for Mul and Add constants
                  mul_const = oh.make_tensor_value_info(
                      model.make_new_valueinfo_name(), TensorProto.FLOAT, A.shape
                  )
                  graph.value_info.append(mul_const)
                  model.set_initializer(mul_const.name, A)
                  mul_output = oh.make_tensor_value_info(
                      model.make_new_valueinfo_name(), TensorProto.FLOAT, data_shape
                  )
                  graph.value_info.append(mul_output)
                  add_const = oh.make_tensor_value_info(
                      model.make_new_valueinfo_name(), TensorProto.FLOAT, B.shape
                  )
                  graph.value_info.append(add_const)
                  model.set_initializer(add_const.name, B)
                  # create Mul and Add nodes to replace the batchnorm
                  mul_node = oh.make_node(
                      "Mul", [bn_input, mul_const.name], [mul_output.name]
                  )
                  add_node = oh.make_node(
                      "Add", [mul_output.name, add_const.name], [bn_output]
                  )
                  # insert where the batchnorm is to preserve topological ordering
                  graph.node.insert(node_ind, mul_node)
                  graph.node.insert(node_ind + 1, add_node)
                  # remove old nodes
                  graph.node.remove(n)
                  if consumer is not None:
                      if consumer.op_type == "Squeeze":
                          graph.node.remove(consumer)
                  if producer is not None:
                      if producer.op_type == "Unsqueeze":
                          graph.node.remove(producer)
          model = model.transform(InferShapes())
          return (model, graph_modified)
  