{"Implements", Func, 5},
		{"ImportMode", Type, 6},
		{"Importer", Type, 5},
		{"ImporterFrom", Type, 6},
		{"Info", Type, 5},
		{"Info.Defs", Field, 5},
		{"Info.FileVersions", Field, 22},
		{"Info.Implicits", Field, 5},
		{"Info.InitOrder", Field, 5},
		{"Info.Instances", Field, 18},
		{"Info.Scopes", Field, 5},
		{"Info.Selections", Field, 5},
		{"Info.Types", Field, 5},
		{"Info.Uses", Field, 5},

            "type": "string"
          },
          "schedulerName": {

Comparison with `numpy.einsum`:

 * This Op only supports unary and binary forms of `numpy.einsum`.
 * This Op does not support implicit form. (i.e. equations without `->`).
 * This Op also supports repeated indices in the output subscript, which is not
   supported by `numpy.einsum`.
@end_compatibility
  }];

  let arguments = (ins

        and works in the DTensor training use case.
    *   Improved masking support for
        [`tf.keras.layers.MultiHeadAttention`](https://www.tensorflow.org/api_docs/python/tf/keras/layers/MultiHeadAttention).
        *   Implicit masks for `query`, `key` and `value` inputs will
            automatically be used to compute a correct attention mask for the
            layer. These padding masks will be combined with any