class_docstr: A data loader that performs mini-batch sampling from link information,
  using a generic :class:`~torch_geometric.sampler.BaseSampler`
  implementation that defines a
  :meth:`~torch_geometric.sampler.BaseSampler.sample_from_edges` function and
  is supported on the provided input :obj:`data` object.
  
  .. note::
      Negative sampling is currently implemented in an approximate
      way, *i.e.* negative edges may contain false negatives.
  
  Args:
      data (Any): A :class:`~torch_geometric.data.Data`,
          :class:`~torch_geometric.data.HeteroData`, or
          (:class:`~torch_geometric.data.FeatureStore`,
          :class:`~torch_geometric.data.GraphStore`) data object.
      link_sampler (torch_geometric.sampler.BaseSampler): The sampler
          implementation to be used with this loader.
          Needs to implement
          :meth:`~torch_geometric.sampler.BaseSampler.sample_from_edges`.
          The sampler implementation must be compatible with the input
          :obj:`data` object.
      edge_label_index (Tensor or EdgeType or Tuple[EdgeType, Tensor]):
          The edge indices, holding source and destination nodes to start
          sampling from.
          If set to :obj:`None`, all edges will be considered.
          In heterogeneous graphs, needs to be passed as a tuple that holds
          the edge type and corresponding edge indices.
          (default: :obj:`None`)
      edge_label (Tensor, optional): The labels of edge indices from which to
          start sampling from. Must be the same length as
          the :obj:`edge_label_index`. (default: :obj:`None`)
      edge_label_time (Tensor, optional): The timestamps of edge indices from
          which to start sampling from. Must be the same length as
          :obj:`edge_label_index`. If set, temporal sampling will be
          used such that neighbors are guaranteed to fulfill temporal
          constraints, *i.e.*, neighbors have an earlier timestamp than
          the ouput edge. The :obj:`time_attr` needs to be set for this
          to work. (default: :obj:`None`)
      neg_sampling (NegativeSampling, optional): The negative sampling
          configuration.
          For negative sampling mode :obj:`"binary"`, samples can be accessed
          via the attributes :obj:`edge_label_index` and :obj:`edge_label` in
          the respective edge type of the returned mini-batch.
          In case :obj:`edge_label` does not exist, it will be automatically
          created and represents a binary classification task (:obj:`0` =
          negative edge, :obj:`1` = positive edge).
          In case :obj:`edge_label` does exist, it has to be a categorical
          label from :obj:`0` to :obj:`num_classes - 1`.
          After negative sampling, label :obj:`0` represents negative edges,
          and labels :obj:`1` to :obj:`num_classes` represent the labels of
          positive edges.
          Note that returned labels are of type :obj:`torch.float` for binary
          classification (to facilitate the ease-of-use of
          :meth:`F.binary_cross_entropy`) and of type
          :obj:`torch.long` for multi-class classification (to facilitate the
          ease-of-use of :meth:`F.cross_entropy`).
          For negative sampling mode :obj:`"triplet"`, samples can be
          accessed via the attributes :obj:`src_index`, :obj:`dst_pos_index`
          and :obj:`dst_neg_index` in the respective node types of the
          returned mini-batch.
          :obj:`edge_label` needs to be :obj:`None` for :obj:`"triplet"`
          negative sampling mode.
          If set to :obj:`None`, no negative sampling strategy is applied.
          (default: :obj:`None`)
      neg_sampling_ratio (int or float, optional): The ratio of sampled
          negative edges to the number of positive edges.
          Deprecated in favor of the :obj:`neg_sampling` argument.
          (default: :obj:`None`).
      transform (callable, optional): A function/transform that takes in
          a sampled mini-batch and returns a transformed version.
          (default: :obj:`None`)
      transform_sampler_output (callable, optional): A function/transform
          that takes in a :class:`torch_geometric.sampler.SamplerOutput` and
          returns a transformed version. (default: :obj:`None`)
      filter_per_worker (bool, optional): If set to :obj:`True`, will filter
          the returned data in each worker's subprocess.
          If set to :obj:`False`, will filter the returned data in the main
          process.
          If set to :obj:`None`, will automatically infer the decision based
          on whether data partially lives on the GPU
          (:obj:`filter_per_worker=True`) or entirely on the CPU
          (:obj:`filter_per_worker=False`).
          There exists different trade-offs for setting this option.
          Specifically, setting this option to :obj:`True` for in-memory
          datasets will move all features to shared memory, which may result
          in too many open file handles. (default: :obj:`None`)
      custom_cls (HeteroData, optional): A custom
          :class:`~torch_geometric.data.HeteroData` class to return for
          mini-batches in case of remote backends. (default: :obj:`None`)
      **kwargs (optional): Additional arguments of
          :class:`torch.utils.data.DataLoader`, such as :obj:`batch_size`,
          :obj:`shuffle`, :obj:`drop_last` or :obj:`num_workers`.
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code: def __init__(
      self,
      data: Union[Data, HeteroData, Tuple[FeatureStore, GraphStore]],
      link_sampler: BaseSampler,
      edge_label_index: InputEdges = None,
      edge_label: OptTensor = None,
      edge_label_time: OptTensor = None,
      neg_sampling: Optional[NegativeSampling] = None,
      neg_sampling_ratio: Optional[Union[int, float]] = None,
      transform: Optional[Callable] = None,
      transform_sampler_output: Optional[Callable] = None,
      filter_per_worker: Optional[bool] = None,
      custom_cls: Optional[HeteroData] = None,
      input_id: OptTensor = None,
      **kwargs,
  ):
      if filter_per_worker is None:
          filter_per_worker = infer_filter_per_worker(data)
  
      # Remove for PyTorch Lightning:
      kwargs.pop("dataset", None)
      kwargs.pop("collate_fn", None)
      # Save for PyTorch Lightning:
      self.edge_label_index = edge_label_index
  
      if neg_sampling_ratio is not None and neg_sampling_ratio != 0.0:
          # TODO: Deprecation warning.
          neg_sampling = NegativeSampling("binary", neg_sampling_ratio)
  
      # Get edge type (or `None` for homogeneous graphs):
      input_type, edge_label_index = get_edge_label_index(
          data, edge_label_index
      )
  
      self.data = data
      self.link_sampler = link_sampler
      self.neg_sampling = NegativeSampling.cast(neg_sampling)
      self.transform = transform
      self.transform_sampler_output = transform_sampler_output
      self.filter_per_worker = filter_per_worker
      self.custom_cls = custom_cls
  
      if (
          self.neg_sampling is not None
          and self.neg_sampling.is_binary()
          and edge_label is not None
          and edge_label.min() == 0
      ):
          # Increment labels such that `zero` now denotes "negative".
          edge_label = edge_label + 1
  
      if (
          self.neg_sampling is not None
          and self.neg_sampling.is_triplet()
          and edge_label is not None
      ):
          raise ValueError(
              "'edge_label' needs to be undefined for "
              "'triplet'-based negative sampling. Please use "
              "`src_index`, `dst_pos_index` and "
              "`neg_pos_index` of the returned mini-batch "
              "instead to differentiate between positive and "
              "negative samples."
          )
  
      self.input_data = EdgeSamplerInput(
          input_id=input_id,
          row=edge_label_index[0],
          col=edge_label_index[1],
          label=edge_label,
          time=edge_label_time,
          input_type=input_type,
      )
  
      iterator = range(edge_label_index.size(1))
      super().__init__(iterator, collate_fn=self.collate_fn, **kwargs)
  
file_path: pytorch_geometric/torch_geometric/loader/link_loader.py
imports: from typing import Any, Callable, Iterator, List, Optional, Tuple, Union
  import torch
  from torch import Tensor
  from torch_geometric.data import Data, FeatureStore, GraphStore, HeteroData
  from torch_geometric.loader.base import DataLoaderIterator
  from torch_geometric.loader.mixin import AffinityMixin, LogMemoryMixin, MultithreadingMixin
  from torch_geometric.loader.utils import filter_custom_hetero_store, filter_custom_store, filter_data, filter_hetero_data, get_edge_label_index, infer_filter_per_worker
  from torch_geometric.sampler import BaseSampler, EdgeSamplerInput, HeteroSamplerOutput, NegativeSampling, SamplerOutput
  from torch_geometric.typing import InputEdges, OptTensor
label_desc: Use image node_id 1 to create a new LinkLoader object from inherited base classes: torch.utils.data, AffinityMixin, MultithreadingMixin, LogMemoryMixin with example: obj = LinkLoader(data, link_sampler, edge_label_index, edge_label, edge_label_time, neg_sampling, neg_sampling_ratio, transform, transform_sampler_output, filter_per_worker, custom_cls, input_id)