// Type utilities needed for implementing typed and type-parameterized
// tests.  This file is generated by a SCRIPT.  DO NOT EDIT BY HAND!
//
// Currently we support at most 50 types in a list, and at most 50
// type-parameterized tests in one type-parameterized test case.
// Please contact ******@****.*** if you need
// more.

#ifndef GTEST_INCLUDE_GTEST_INTERNAL_GTEST_TYPE_UTIL_H_

					Name:       "hasParams",
					Expression: `params.foo == "okay"`,
				}},
				Validations: []admissionregistration.Validation{{
					Expression: "object.x < 50",
				}},
			},
		},
		expectedError: "",
	},
		{
			name: "expressions that are not changed must be compiled using the StoredExpression environment",

}

const (
	// WorkerMaxLimit max number of workers per node for "fast" mode
	WorkerMaxLimit = 500

	// WorkerMinLimit min number of workers per node for "slow" mode
	WorkerMinLimit = 50

	// WorkerAutoDefault is default number of workers for "auto" mode
	WorkerAutoDefault = 100

	// MRFWorkerMaxLimit max number of mrf workers per node for "fast" mode
	MRFWorkerMaxLimit = 8

    static const int rank_two = 2;
    // This converts a gather op to multiple slice ops, cap the number of slice
    // ops allowed.
    static const int max_batch_size = 50;

    // Can only convert with static shaped gather.
    ShapedType operand_type = mlir::cast<ShapedType>(operand.getType());
    ShapedType start_indices_type =

  // of the `first_arg_op` kind (e.g. tf.Mul) are stored in `exceptions`. If
  // there are too many exceptions, it might not be cost effective to apply the
  // concat hoisting optimization here.
  // Setting the threshold to be 50% as a simple cost model heuristic. e.g. If 1
  // out of 2 concat inputs is an exception, we don't apply the hoist. If it's 1
  // out of 3, we do.
  const float exception_pct_threshold = 0.5;

	for len(w)%n != 0 {
		if w[0] != 0 {
			return false
		}
		w = w[1:]
	}
	for len(w) >= n {
		if w[0] != 0 || w[1] != 0 || w[2] != 0 || w[3] != 0 ||
			w[4] != 0 || w[5] != 0 || w[6] != 0 || w[7] != 0 ||
			w[8] != 0 || w[9] != 0 || w[10] != 0 || w[11] != 0 ||
			w[12] != 0 || w[13] != 0 || w[14] != 0 || w[15] != 0 ||
			w[16] != 0 || w[17] != 0 || w[18] != 0 || w[19] != 0 ||

    )
    saved_model_save.save(model, self._input_saved_model_path)

    data_gen = self._create_data_generator(
        input_key='input_tensor',
        shape=[50],
        minval=0,
        maxval=64,
        dtype=dtypes.int32,
    )

    tags = {tag_constants.SERVING}

    calibration_data_dir = self.create_tempdir('calibration_data').full_path

  // Operand 1 (scores) should be a 1-dim tensor
  TFL_OperandHasRank<1, 1>,
  // Other operands are scalar params.
  TFL_OperandHasRank<2, 0>, TFL_OperandHasRank<3, 0>,
  TFL_OperandHasRank<4, 0>, TFL_OperandHasRank<5, 0>]> {
  let summary = [{
Greedily selects a subset of bounding boxes in descending order of score,
  }];

  let description = [{
pruning away boxes that have high intersection-over-union (IOU) overlap

            "format": "int32",
            "type": "integer"
          },
          "queues": {
            "default": 0,

            "format": "int32",
            "type": "integer"
          },
          "queues": {
            "default": 0,