class_docstr: A data loader which merges succesive events of a
  :class:`torch_geometric.data.TemporalData` to a mini-batch.
  
  Args:
      data (TemporalData): The :obj:`~torch_geometric.data.TemporalData`
          from which to load the data.
      batch_size (int, optional): How many samples per batch to load.
          (default: :obj:`1`)
      neg_sampling_ratio (float, optional): The ratio of sampled negative
          destination nodes to the number of postive destination nodes.
          (default: :obj:`0.0`)
      **kwargs (optional): Additional arguments of
          :class:`torch.utils.data.DataLoader`.
code: def __init__(
      self,
      data: TemporalData,
      batch_size: int = 1,
      neg_sampling_ratio: float = 0.0,
      **kwargs,
  ):
      # Remove for PyTorch Lightning:
      kwargs.pop("dataset", None)
      kwargs.pop("collate_fn", None)
      kwargs.pop("shuffle", None)
  
      self.data = data
      self.events_per_batch = batch_size
      self.neg_sampling_ratio = neg_sampling_ratio
  
      if neg_sampling_ratio > 0:
          self.min_dst = int(data.dst.min())
          self.max_dst = int(data.dst.max())
  
      if kwargs.get("drop_last", False) and len(data) % batch_size != 0:
          arange = range(0, len(data) - batch_size, batch_size)
      else:
          arange = range(0, len(data), batch_size)
  
      super().__init__(
          arange, 1, shuffle=False, collate_fn=self, **kwargs
      )
  
imports: from typing import List
  import torch
  from torch_geometric.data import TemporalData
label_desc: Use image node_id 1 to create a new TemporalDataLoader object from inherited base classes: torch.utils.data with example: obj = TemporalDataLoader(data, batch_size, neg_sampling_ratio)