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		{"ToolDir", Var, 0},
	},
	"go/build/constraint": {
		{"(*AndExpr).Eval", Method, 16},
		{"(*AndExpr).String", Method, 16},
		{"(*NotExpr).Eval", Method, 16},
		{"(*NotExpr).String", Method, 16},
		{"(*OrExpr).Eval", Method, 16},
		{"(*OrExpr).String", Method, 16},
		{"(*SyntaxError).Error", Method, 16},
		{"(*TagExpr).Eval", Method, 16},
		{"(*TagExpr).String", Method, 16},
		{"AndExpr", Type, 16},

        to store weights, allowing a dramatic speedup for large sparse models.
    *   Enable the Keras compile API `experimental_run_tf_function` flag by
        default. This flag enables single training/eval/predict execution path.
        With this 1. All input types are converted to `Dataset`. 2. When
        distribution strategy is not specified this goes through the no-op

Usage:
  ```python
  import tensorflow as tf
  a = [1, 10, 26.9, 2.8, 166.32, 62.3]
  b = tf.math.argmax(input = a)
  c = tf.keras.backend.eval(b)
  # c = 4
  # here a[4] = 166.32 which is the largest element of a across axis 0
  ```
  }];

  let arguments = (ins