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 the dimension in which the attribute :obj:`key` with
  content :obj:`value` gets concatenated when creating batches.
  
  .. note::
  
      This method is for internal use only, and should only be overridden
      if the batch concatenation process is corrupted for a specific data
      attribute.
docstr_tok: ["Returns","the","dimension","in","which","the","attribute",":","obj",":","`","key","`","with","content",":","obj",":","`","value","`","gets","concatenated","when","creating","batches",".","..","note",":",":","This","method","is","for","internal","use","only",",","and","should","only","be","overridden","if","the","batch","concatenation","process","is","corrupted","for","a","specific","data","attribute","."]
code: def cat_dim(self, key, value):
      r"""Returns the dimension in which the attribute :obj:`key` with
      content :obj:`value` gets concatenated when creating batches.
  
      .. note::
  
          This method is for internal use only, and should only be overridden
          if the batch concatenation process is corrupted for a specific data
          attribute.
      """
      # `*index*` and `*face*` should be concatenated in the last dimension,
      # everything else in the first dimension.
      return -1 if bool(re.search("(index|face)", key)) else 0
  
imports: import re
  import torch
label_desc: Use image node_id 10 for calling the Data obj's underlying member method code with example usage: obj.cat_dim(key, value) and returns: unknown