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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 get_gates(self, x):
 """Return the bucket id of the given tensor. Args: x (tf.Tensor):
 float32 of shape [length, depth] Returns: tf.Tensor: One-hot vector int64 of shape [heads, length, nb_buckets] containing the id of the bucket """


    # The balance loss don't propagate to the rest of the network
    x = tf.stop_gradient(x)
    # [length, depth] * [depth, nb_vectors * replicat]
    x = tf.matmul(x, self.t_vectors)
    # [length, nb_vector * replicat]
    x = tf.sign(x)  # Get on which side of the hyperplane the keys are.

    # x = tf.reshape(x, [-1, nb_replicat, nb_vector])
    # [length, replicat, nb_vector] * [nb_vector, 2^nb_vector - 1]

    x = tf.matmul(x, self.t_group, transpose_b=True) / self.nb_hyperplanes
    # We get a similarity score for each of the group between [-1, 1]
    # [length, (replicat,) 2^nb_vector - 1]
    # Do an argmax to get the most likely group for each replicat
    x = tf.argmax(x, axis=-1)
    # [length(, replicat)]
    # One-hot for compatibility with the sparse dispatcher
    x = tf.one_hot(x, self.nb_buckets)
    # TODO(epot): Use a loss to force an even distribution
    return x