parameters: (tensor_in, n_classes, embedding_size, name)
docstring: Creates an embedding for categorical variable with given number of classes.
  
    Args:
      tensor_in: Input tensor with class identifier (can be batch or
        N-dimensional).
      n_classes: Number of classes.
      embedding_size: Size of embedding vector to represent each class.
      name: Name of this categorical variable.
    Returns:
      Tensor of input shape, with additional dimension for embedding.
  
    Example:
      Calling categorical_variable([1, 2], 5, 10, "my_cat"), will return 2 x 10
      tensor, where each row is representation of the class.
docstring_summary: Creates an embedding for categorical variable with given number of classes.
docstring_tokens: Creates an embedding for categorical variable with given number of classes .
function: def categorical_variable(tensor_in, n_classes, embedding_size, name):
    """Creates an embedding for categorical variable with given number of classes.
  
    Args:
      tensor_in: Input tensor with class identifier (can be batch or
        N-dimensional).
      n_classes: Number of classes.
      embedding_size: Size of embedding vector to represent each class.
      name: Name of this categorical variable.
    Returns:
      Tensor of input shape, with additional dimension for embedding.
  
    Example:
      Calling categorical_variable([1, 2], 5, 10, "my_cat"), will return 2 x 10
      tensor, where each row is representation of the class.
    """
    with vs.variable_scope(name):
      embeddings = vs.get_variable(name + '_embeddings',
                                   [n_classes, embedding_size])
      return embedding_lookup(embeddings, tensor_in)
function_tokens: def categorical_variable ( tensor_in , n_classes , embedding_size , name ) : with vs . variable_scope ( name ) : embeddings = vs . get_variable ( name + '_embeddings' , [ n_classes , embedding_size ] ) return embedding_lookup ( embeddings , tensor_in )