for _, k := range index {
					kv, kconst := parse(k)
					// Avoid errors that would make the program not compile.
					// Those are tested by slice3err.go.
					switch {
					case iconst && jconst && iv > jv,
						jconst && kconst && jv > kv,
						iconst && kconst && iv > kv,
						iconst && base == "array" && iv > Cap,
						jconst && base == "array" && jv > Cap,
						kconst && base == "array" && kv > Cap:
						continue

echo '// Code generated by the command above; DO NOT EDIT.'
echo
go tool cgo -godefs -- "$@" _const.go >_error.out
cat _error.out | grep -vf _error.grep | grep -vf _signal.grep
echo
echo '// Errors'
echo 'const ('
cat _error.out | grep -f _error.grep | sed 's/=\(.*\)/= Errno(\1)/'
echo ')'

echo
echo '// Signals'
echo 'const ('
cat _error.out | grep -f _signal.grep | sed 's/=\(.*\)/= Signal(\1)/'
echo ')'

# RUN: tf-mlir-translate -graphdef-to-mlir -tf-enable-shape-inference-on-import=false %s -o - | FileCheck %s

node {
  name: "bf16_scalar"
  op: "Const"
  attr {
    key: "dtype"
    value {
      type: DT_BFLOAT16
    }
  }
  attr {
    key: "value"
    value {
      tensor {
        dtype: DT_BFLOAT16
        tensor_shape {
        }
        half_val: 0
        # CHECK: value = dense<0.000000e+00> : tensor<bf16>

	_ = x[_Rbrack-15]
	_ = x[_Rbrace-16]
	_ = x[_Comma-17]
	_ = x[_Semi-18]
	_ = x[_Colon-19]
	_ = x[_Dot-20]
	_ = x[_DotDotDot-21]
	_ = x[_Break-22]
	_ = x[_Case-23]
	_ = x[_Chan-24]
	_ = x[_Const-25]
	_ = x[_Continue-26]
	_ = x[_Default-27]
	_ = x[_Defer-28]
	_ = x[_Else-29]
	_ = x[_Fallthrough-30]
	_ = x[_For-31]
	_ = x[_Func-32]
	_ = x[_Go-33]
	_ = x[_Goto-34]
	_ = x[_If-35]

  tensorflow::Algorithm tensorflow_rng =
      tensorflow::ToTensorflowAlgorithm(xla_rng);

  OpBuilder opbuilder(get_alg_op);

  auto tf_const = opbuilder.create<TF::ConstOp>(
      get_alg_op->getLoc(), opbuilder.getI32IntegerAttr((int)tensorflow_rng));

  get_alg_op->replaceAllUsesWith(tf_const);
  get_alg_op->erase();
  return success();
}

void TFXLADeviceSpecificTransforms::runOnOperation() {

node {
  name: "Quantized_Constant"
  op: "Const"
  attr {
    key: "dtype"
    value {
      type: DT_QUINT8
    }
  }
  attr {
    key: "value"
    value {
      tensor {
        dtype: DT_QUINT8
        tensor_shape {
        }
        int_val: 7
      }
    }
  }
}
library {
}
versions {
  producer: 70
}

# CHECK: tf.Const

    }
  }
}

inline float GetSparsity(const int num_zeros, const int num_elements) {
  return (1.0 * num_zeros / num_elements);
}

float CalculateRandomSparsity(const ElementsAttr& attr,
                              const ShapedType& type) {
  int num_elements = type.getNumElements();
  int num_zeros = 0;

  if (mlir::isa<FloatType>(type.getElementType())) {
    for (const auto val : attr.getValues<APFloat>()) {

// Use of this source code is governed by a BSD-style
// license that can be found in the LICENSE file.

extern void Step(int, int, int *, int *);
extern void DoStep(int, int, int, int, int, int, int *, int *);

// Use of this source code is governed by a BSD-style
// license that can be found in the LICENSE file.

extern void Step(int, int, int *, int *);
extern void DoStep(int, int, int, int, int, int, int *, int *);

			objabi.R_LOONG64_TLS_IE_HI,
			objabi.R_LOONG64_TLS_IE_LO,
			objabi.R_LOONG64_GOT_HI,
			objabi.R_LOONG64_GOT_LO:
			return val, 1, true
		}
	}

	const isOk = true
	const noExtReloc = 0

	switch r.Type() {
	case objabi.R_CONST:
		return r.Add(), noExtReloc, isOk
	case objabi.R_GOTOFF:
		return ldr.SymValue(r.Sym()) + r.Add() - ldr.SymValue(syms.GOT), noExtReloc, isOk
	case objabi.R_LOONG64_ADDR_HI,