return size;
              }
            };
          }
        };
      }

      @Override
      public int size() {
        return IntMath.binomial(index.size(), size);
      }

      @Override
      public String toString() {
        return "Sets.combinations(" + index.keySet() + ", " + size + ")";
      }
    };
  }

		g.p("\tprintln(")
		for j := 0; j < n; j++ {
			if j > 0 {
				g.p(", ")
			}
			g.p("p%d_%d", i, j)
		}
		g.p(")\n")
	}
	g.p("}\n\n")
}

func (g *gen) binomials(n int) {
	g.p(`// Binomials
const (
`)
	for j := 0; j <= n; j++ {
		if j > 0 {
			g.p("\n")
		}
		for k := 0; k <= j; k++ {
			g.p("\tb%d_%d = f%d / (f%d*f%d)\n", j, k, j, k, j-k)
		}
	}
	g.p(")\n\n")

def TF_StatelessRandomBinomialOp : TF_Op<"StatelessRandomBinomial", [Pure, TF_NoConstantFold]> {
  let summary = [{
Outputs deterministic pseudorandom random numbers from a binomial distribution.
  }];

  let description = [{
Outputs random values from a binomial distribution.

The outputs are a deterministic function of `shape`, `seed`, `counts`, and `probs`.
  }];

  let arguments = (ins

    *   Add a new "result_type" parameter to `tf.strings.split`.
    *   Add name argument to `tf.string_split` and `tf.strings_split`.
    *   Extend `tf.strings.split` to support inputs with any rank.
    *   Added `tf.random.binomial`.
    *   Added `key` and `skip` methods to `random.experimental.Generator`.
    *   Extend `tf.function` with basic support for CompositeTensors arguments
        (such as `SparseTensor` and `RaggedTensor`).

		{"(*Float).UnmarshalText", Method, 6},
		{"(*Int).Abs", Method, 0},
		{"(*Int).Add", Method, 0},
		{"(*Int).And", Method, 0},
		{"(*Int).AndNot", Method, 0},
		{"(*Int).Append", Method, 6},
		{"(*Int).Binomial", Method, 0},
		{"(*Int).Bit", Method, 0},
		{"(*Int).BitLen", Method, 0},
		{"(*Int).Bits", Method, 0},
		{"(*Int).Bytes", Method, 0},
		{"(*Int).Cmp", Method, 0},
		{"(*Int).CmpAbs", Method, 10},