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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def build_tensor_serving_input_receiver_fn(shape, dtype=tf.float32, batch_size=1):
 """Returns a input_receiver_fn that can be used during serving. This expects examples to come through as float tensors, and simply wraps them as TensorServingInputReceivers. Arguably, this should live in tf.estimator.export. Testing here first. Args: shape: list representing target size of a single example. dtype: the expected datatype for the input example batch_size: number of input tensors that will be passed for prediction Returns: A function that itself returns a TensorServingInputReceiver. """

  def serving_input_receiver_fn():
    # Prep a placeholder where the input example will be fed in
    features = tf.placeholder(
        dtype=dtype, shape=[batch_size] + shape, name='input_tensor')

    return tf.estimator.export.TensorServingInputReceiver(
        features=features, receiver_tensors=features)

  return serving_input_receiver_fn