code: def create_mock_batch(
      batch_size,
      sequence_length,
      number_of_nodes,
      edge_per_node,
      in_channels,
      number_of_classes,
  ):
      """
      Creating a mock batch of sequences
      """
      batch = torch.zeros(
          batch_size, sequence_length, number_of_nodes, in_channels
      )
      batch_targets = torch.zeros(
          batch_size, number_of_nodes, dtype=torch.long
      )
  
      for b in range(batch_size):
          (
              input_sequence,
              targets,
              edge_index,
              edge_weight,
          ) = create_mock_sequence(
              sequence_length,
              number_of_nodes,
              edge_per_node,
              in_channels,
              number_of_classes,
          )
          batch[b] = input_sequence
          batch_targets[b] = targets
  
      return batch, batch_targets, edge_index, edge_weight
  
imports: import torch
  import math
  import numpy
  import networkx
  from torch_geometric.data import Data
  from torch_geometric.utils import barabasi_albert_graph
  from torch_geometric.transforms import LaplacianLambdaMax
  from torch_geometric_temporal.nn.attention import TemporalConv, STConv, ASTGCN, MSTGCN, MTGNN, ChebConvAttention, AAGCN, GraphAAGCN, DNNTSP
  from torch_geometric_temporal.nn.attention import GMAN, SpatioTemporalAttention, SpatioTemporalEmbedding
label_desc: Use image node_id 5 for calling a global function with example usage: create_mock_batch(batch_size, sequence_length, number_of_nodes, edge_per_node, in_channels, number_of_classes) and returns: batch,  batch_targets,  edge_index,  edge_weight