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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 graph_attention(q, k, v, bias, dropout_rate=0.0, image_shapes=None, name=None, make_image_summary=True, save_weights_to=None, dropout_broadcast_dims=None, adjacency_matrix=None, num_edge_types=5):
 """graph attention. Args: q: a Tensor with shape [batch, heads, length_q, depth_k] k: a Tensor with shape [batch, heads, length_kv, depth_k] v: a Tensor with shape [batch, heads, length_kv, depth_v] bias: bias Tensor (see attention_bias()) dropout_rate: a floating point number image_shapes: optional tuple of integer scalars. see comments for attention_image_summary() name: an optional string make_image_summary: True if you want an image summary. save_weights_to: an optional dictionary to capture attention weights for vizualization; the weights tensor will be appended there under a string key created from the variable scope (including name). dropout_broadcast_dims: an optional list of integers less than 4 specifying in which dimensions to broadcast the dropout decisions. saves memory. adjacency_matrix: optional matrix of [batch, length, length] ids indicating edge type num_edge_types: an int indicating number of edge types Returns: A Tensor of shape [batch, length, depth(q)] """

  with tf.variable_scope(
      name, default_name="dot_product_attention", values=[q, k, v]) as scope:
    # [batch, num_heads, query_length, memory_length]
    logits = tf.matmul(q, k, transpose_b=True)
    if adjacency_matrix is not None:
      key_head_depth = common_layers.shape_list(q)[-1]
      adjacency_vectors = make_edge_vectors(
          adjacency_matrix,
          num_edge_types,
          key_head_depth,
          name=name)
      # transposing q to be [batch, length_q, heads, depth_k]
      # to allow for matmul with [batch, length_q, length_q, depth_k]
      q_t = tf.transpose(q, [0, 2, 1, 3])
      adj_logits = tf.matmul(q_t, adjacency_vectors, transpose_b=True)
      logits += tf.transpose(adj_logits, [0, 2, 1, 3])
      # [batch, depth, num_nodes, num_nodes]
    if bias is not None:
      logits += bias
    weights = tf.nn.softmax(logits, name="attention_weights")
    if save_weights_to is not None:
      save_weights_to[scope.name] = weights
    # dropping out the attention links for each of the heads
    weights = common_layers.dropout_with_broadcast_dims(
        weights, 1.0 - dropout_rate, broadcast_dims=dropout_broadcast_dims)
    if common_layers.should_generate_summaries() and make_image_summary:
      common_attention.attention_image_summary(weights, image_shapes)
    return tf.matmul(weights, v)