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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_weights(model_hparams, vocab_size, hidden_dim=None):
 """Create or get concatenated embedding or softmax variable. Args: model_hparams: HParams, model hyperparmeters. vocab_size: int, vocabulary size. hidden_dim: dim of the variable. Defaults to _model_hparams' hidden_size Returns: a list of num_shards Tensors. """

  if hidden_dim is None:
    hidden_dim = model_hparams.hidden_size
  num_shards = model_hparams.symbol_modality_num_shards
  shards = []
  for i in range(num_shards):
    shard_size = (vocab_size // num_shards) + (
        1 if i < vocab_size % num_shards else 0)
    var_name = "weights_%d" % i
    shards.append(
        tf.get_variable(
            var_name, [shard_size, hidden_dim],
            initializer=tf.random_normal_initializer(0.0, hidden_dim**-0.5)))
  if num_shards == 1:
    ret = shards[0]
  else:
    ret = tf.concat(shards, 0)
  # Convert ret to tensor.
  if not tf.executing_eagerly():
    ret = common_layers.convert_gradient_to_tensor(ret)
  return ret