class_docstr: Represent a single graph with additional attributes.
class_docstr_tok: ["Represent","a","single","graph","with","additional","attributes","."]
docstr: Remove all the edges and the corresponding features and attributes in GraphData.
  
  Examples
  --------
  >>> g = GraphData()
  >>> g.add_nodes(10)
  >>> g.add_edges(list(range(0, 9, 1)), list(range(1, 10, 1)))
  # Added some feature tensors to the edges
  >>> g.edge_features['random'] = torch.rand((9, 1024, 1024))
  # Remove all edges and the corresponding data. The tensor memory is freed now.
  >>> g.remove_all_edges()
  
  Returns
  -------
  None
code: def remove_all_edges(self):
      """
      Remove all the edges and the corresponding features and attributes in GraphData.
  
      Examples
      --------
      >>> g = GraphData()
      >>> g.add_nodes(10)
      >>> g.add_edges(list(range(0, 9, 1)), list(range(1, 10, 1)))
      # Added some feature tensors to the edges
      >>> g.edge_features['random'] = torch.rand((9, 1024, 1024))
      # Remove all edges and the corresponding data. The tensor memory is freed now.
      >>> g.remove_all_edges()
  
      Returns
      -------
      None
      """
  
      self._edge_indices = EdgeIndex(src=[], tgt=[])
      self._nids_eid_mapping = nids_eid_mapping_factory()
      self._edge_features = edge_feature_factory(res_init_edge_features)
      self._edge_attributes = edge_attribute_factory()
  
imports: import os
  import warnings
  from collections import namedtuple
  from typing import Any, Callable, Dict, List, Tuple, Union
  import dgl
  import scipy.sparse
  import torch
  from torch.nn.utils.rnn import pad_sequence
  from .utils import EdgeNotFoundException, SizeMismatchException, check_and_expand, entail_zero_padding, int_to_list, slice_to_list
  from .views import BatchEdgeFeatView, BatchNodeFeatView, EdgeView, NodeFeatView, NodeView
label_desc: Use image node_id 19 for calling the GraphData obj's underlying member method code with example usage: obj.remove_all_edges() without return types