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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 call(self, x):
 """Get token embeddings of x. Args: x: An int64 tensor with shape [batch_size, length] Returns: embeddings: float32 tensor with shape [batch_size, length, embedding_size] padding: float32 tensor with shape [batch_size, length] indicating the locations of the padding tokens in x. """

    with tf.name_scope("embedding"):
      embeddings = tf.gather(self.shared_weights, x)

      # Scale embedding by the sqrt of the hidden size
      embeddings *= self.hidden_size ** 0.5

      # Create binary array of size [batch_size, length]
      # where 1 = padding, 0 = not padding
      padding = model_utils.get_padding(x)

      # Set all padding embedding values to 0
      embeddings *= tf.expand_dims(1 - padding, -1)
      return embeddings