code: def algebra_inverse(alphabet_size=26, min_depth=0, max_depth=2,
                      nbr_cases=10000):
    
  
    if max_depth < min_depth:
      raise ValueError("max_depth must be greater than or equal to min_depth. "
                       "Got max_depth=%s, min_depth=%s" % (max_depth, min_depth))
  
    alg_cfg = math_dataset_init(alphabet_size)
    for _ in range(nbr_cases):
      sample, target = generate_algebra_inverse_sample(
          alg_cfg.vlist,
          list(alg_cfg.ops.values()), alg_cfg.solve_ops, min_depth, max_depth)
      yield {
          "inputs": alg_cfg.int_encoder(sample),
          "targets": alg_cfg.int_encoder(target)
      }
docs: Generate the algebra inverse dataset.
  
    Each sample is a symbolic math equation involving unknown variables. The
    task is to solve for the given variable. The target is the resulting
    expression.
  
    Args:
      alphabet_size: How many possible variables there are. Max 52.
      min_depth: Minimum depth of the expression trees on both sides of the
          equals sign in the equation.
      max_depth: Maximum depth of the expression trees on both sides of the
          equals sign in the equation.
      nbr_cases: The number of cases to generate.
  
    Yields:
      A dictionary {"inputs": input-list, "targets": target-list} where
      input-list are the tokens encoding the variable to solve for and the math
      equation, and target-list is a list of tokens encoding the resulting math
      expression after solving for the variable.
  
    Raises:
      ValueError: If `max_depth` < `min_depth`.