t.Fatalf("%s:\n\tinput: %s\n\tgot error:  %v\n\twant error: %v", tt.Where, tt.json, err, errWant)
					}
					break
				} else if err != nil {
					t.Fatalf("%s:\n\tinput: %s\n\tgot error:  %v\n\twant error: nil", tt.Where, tt.json, err)
				}
				if !reflect.DeepEqual(got, want) {

			var s2 StringTag
			if err := Unmarshal(got, &s2); err != nil {
				t.Fatalf("%s: Decode error: %v", tt.Where, err)
			}
			if !reflect.DeepEqual(s2, tt.in) {
				t.Fatalf("%s: Decode:\n\tinput: %s\n\tgot:  %#v\n\twant: %#v", tt.Where, indentNewlines(string(got)), s2, tt.in)
			}
		})
	}
}

// byte slices are special even if they're renamed types.
type renamedByte byte

                                e->src()->output_type(e->src_output())};
  }
  for (const auto& input : inputs) {
    replace_builder->Input(input);
  }
  for (const auto& attr : n->attrs()) {
    replace_builder->Attr(attr.first, attr.second);
  }
  auto replace_def = std::make_unique<NodeDef>();
  TF_RETURN_IF_ERROR(replace_builder->Finalize(replace_def.get()));

package norm

import "unicode/utf8"

type input struct {
	str   string
	bytes []byte
}

func inputBytes(str []byte) input {
	return input{bytes: str}
}

func inputString(str string) input {
	return input{str: str}
}

func (in *input) setBytes(str []byte) {
	in.str = ""
	in.bytes = str
}

func (in *input) setString(str string) {
	in.str = str
	in.bytes = nil
}

	"strings"
	"text/scanner"

	"cmd/asm/internal/flags"
	"cmd/internal/objabi"
	"cmd/internal/src"
)

// Input is the main input: a stack of readers and some macro definitions.
// It also handles #include processing (by pushing onto the input stack)
// and parses and instantiates macro definitions.
type Input struct {
	Stack
	includes        []string
	beginningOfLine bool
	ifdefStack      []bool

 * limitations under the License.
 */
package org.gradle.api.tasks;

import java.lang.annotation.*;

/**
 * <p>Attached to a task property to indicate that the property specifies some input value for the task.</p>
 *
 * <p>This annotation should be attached to the getter method in Java or the property in Groovy.
 * Annotations on setters or just the field in Java are ignored.</p>
 *

// RUN: tac-translate -input-mlir -output-mlir -device-specs=GPU %s -o - 2>&1 | FileCheck %s

module {
func.func @main(%arg0: tensor<1xf32>, %arg1: tensor<1xf32>, %arg2: tensor<1xf32>, %arg3: tensor<1xf32>) -> tensor<2x1xf32> attributes {tf.entry_function = {inputs = "input0,input1,input2,input3", outputs = "output"}} {
  %0 = "tfl.add"(%arg0, %arg1) {fused_activation_function = "RELU6"} : (tensor<1xf32>, tensor<1xf32>) -> tensor<1xf32>

  %10 = "tf.opK"() : () -> tensor<*xi16>
  %11 = "tf.opL"() : () -> tensor<*xi64>
  tf_device.replicate([%0, %1, %2] as %input0: tensor<*xi1>, %9 as %input1: tensor<*xi8>, %10 as %input2: tensor<*xi16>, [%3, %4, %5] as %input3: tensor<*xi32>, [%6, %7, %8] as %input4: tensor<*xf32>, %11 as %input5: tensor<*xi64>) {n = 3 : i32} {
    tf_device.return
  }
  func.return

// CHECK:      %[[OP_A:[a-z0-9]*]] = "tf.opA"

# RUN: tf_tfl_translate -tf-input-arrays=input0,input1 \
# RUN:                  -tf-input-shapes=4:4 \
# RUN:                  -tf-input-data-types=DT_FLOAT,DT_FLOAT \
# RUN:                  -tf-output-arrays=Add \
# RUN:                  -tf-inference-type=DT_QUINT8 \
# RUN:                  -tf-input-min-values='-2,-3' \
# RUN:                  -tf-input-max-values='2,3' \
# RUN:                  --quant-stats=%s.stats \

overlapping-outputs-input-comparison.png...