// Result: 128512 128512 😀 '😀' U+1F600 U+1F600 '😀'

	// Strings are formatted with %v and %s as-is, with %q as quoted strings,
	// and %#q as backquoted strings.
	placeholders := `foo "bar"`
	fmt.Printf("%v %s %q %#q\n", placeholders, placeholders, placeholders, placeholders)
	// Result: foo "bar" foo "bar" "foo \"bar\"" `foo "bar"`

	// Maps formatted with %v show keys and values in their default formats.

			stmt.Close()
			return nil, errf("SELECT on table %q has pre-bound value for where column %q; need a question mark",
				stmt.table, column)
		}
		stmt.placeholders++
		stmt.whereCol = append(stmt.whereCol, boundCol{Column: column, Placeholder: value, Ordinal: stmt.placeholders})
	}
	return stmt, nil
}

// parts are table|col=type,col2=type2
func (c *fakeConn) prepareCreate(stmt *fakeStmt, parts []string) (*fakeStmt, error) {

package aliases

import (
	"go/token"
	"go/types"
)

// Package aliases defines backward compatible shims
// for the types.Alias type representation added in 1.22.
// This defines placeholders for x/tools until 1.26.

// NewAlias creates a new TypeName in Package pkg that
// is an alias for the type rhs.
//
// The enabled parameter determines whether the resulting [TypeName]'s

#include "tensorflow/core/platform/status.h"

namespace tensorflow {

// Builds and returns a `TracingContext` using the default tracing impl.
AbstractContext* BuildFunction(const char* fn_name);

// Creates parameters (placeholders) in the tracing `ctx` using the shape and
// dtype of `inputs`.
Status CreateParamsForInputs(AbstractContext* ctx,
                             absl::Span<AbstractTensorHandle* const> inputs,

   * Preconditions} class use this formatter, for both of the reasons just discussed.
   *
   * <p><b>Warning:</b> Only the exact two-character placeholder sequence {@code "%s"} is
   * recognized.
   *
   * @param template a string containing zero or more {@code "%s"} placeholder sequences. {@code
   *     null} is treated as the four-character string {@code "null"}.

   * Preconditions} class use this formatter, for both of the reasons just discussed.
   *
   * <p><b>Warning:</b> Only the exact two-character placeholder sequence {@code "%s"} is
   * recognized.
   *
   * @param template a string containing zero or more {@code "%s"} placeholder sequences. {@code
   *     null} is treated as the four-character string {@code "null"}.

TEST(ShapeInferenceTest, Basics) {
  Scope root = Scope::NewRootScope().ExitOnError();
  auto a = ops::Placeholder(root.WithOpName("A"), DT_FLOAT,
                            ops::Placeholder::Shape({2, 3}));
  auto b = ops::Placeholder(root.WithOpName("B"), DT_FLOAT,
                            ops::Placeholder::Shape({3}));
  auto c = ops::Placeholder(root.WithOpName("C"), DT_FLOAT);
  auto d = ops::Add(root.WithOpName("D"), a, b);

        when:
        writeSettingsFile(locations.userSettingsFile, repo1)

        then:
        locator.localMavenRepository == repo1
    }

    def "replaces placeholders for system properties and environment variables"() {
        when:
        writeSettingsFile(locations.userSettingsFile, tmpDir.file('${sys.prop}/${env.ENV_VAR}'))

        and:

    const ScaleFn& fn, const ScaleDecomposeFn& dfn) {
  return specs_[kernel].Add(signature, {ScaleConstraintType::CustomScale, fn});
}

namespace ph = std::placeholders;

LogicalResult DeviceTarget::RegisterKernel(
    llvm::StringRef kernel, const KernelSpecs::Signature& signature,
    const ScaleConstraintType constraint) {

#include "tensorflow/compiler/mlir/lite/utils/generated_op_quant_spec_getters.inc"

bool PrepareQuantizePass::RemoveRedundantStats(func::FuncOp func) {
  return RemoveRedundantStatsOps(
      func, std::bind(GetOpQuantSpec, std::placeholders::_1,
                      quant_specs_.disable_per_channel_for_dense_layers));
}

static Value Quantized(Operation* user) {
  if (auto q = llvm::dyn_cast_or_null<quantfork::QuantizeCastOp>(user)) {