class_docstr: A plain python object modeling a single graph with various
  (optional) attributes:
  
  Args:
      x (Tensor, optional): Node feature matrix with shape :obj:`[num_nodes,
          num_node_features]`. (default: :obj:`None`)
      edge_index (LongTensor, optional): Graph connectivity in COO format
          with shape :obj:`[2, num_edges]`. (default: :obj:`None`)
      edge_attr (Tensor, optional): Edge feature matrix with shape
          :obj:`[num_edges, num_edge_features]`. (default: :obj:`None`)
      y (Tensor, optional): Graph or node targets with arbitrary shape.
          (default: :obj:`None`)
      pos (Tensor, optional): Node position matrix with shape
          :obj:`[num_nodes, num_dimensions]`. (default: :obj:`None`)
  
  The data object is not restricted to these attributes and can be extented
  by any other additional data.
docstr: Returns :obj:`True`, if the attribute :obj:`key` is present in the
  data.
docstr_tok: ["Returns",":","obj",":","`","True","`",",","if","the","attribute",":","obj",":","`","key","`","is","present","in","the","data","."]
code: def __contains__(self, key):
      r"""Returns :obj:`True`, if the attribute :obj:`key` is present in the
      data."""
      return key in self.keys
  
imports: import re
  import torch
label_desc: Use image node_id 7 for calling the Data obj's underlying member method code with example usage: obj.__contains__(key) and returns: unknown