}
}

// Keep this code in sync with cmd/compile/internal/walk/builtin.go:walkUnsafeSlice
func unsafeslice64(et *_type, ptr unsafe.Pointer, len64 int64) {
	len := int(len64)
	if int64(len) != len64 {
		panicunsafeslicelen1(getcallerpc())
	}
	unsafeslice(et, ptr, len)
}

func unsafeslicecheckptr(et *_type, ptr unsafe.Pointer, len64 int64) {
	unsafeslice64(et, ptr, len64)

      attr {
        key: "dtype"
        value {
          type: DT_FLOAT
        }
      }
      attr {
        key: "value"
        value {
          tensor {
            dtype: DT_FLOAT
            tensor_shape {
            }
            float_val: 7
          }
        }
      }
    }
    ret {
      key: "constant"
      value: "X:output:0"
    }
    # This should be modeled as unit attribute

      %outputs, %control = tf_executor.island wraps "tf.Const"() {device = "", dtype = f32, value = dense<[]> : tensor<0xf32>} : () -> tensor<0xf32>
      %outputs_0, %control_1 = tf_executor.island wraps "tf.Const"() {device = "", dtype = f32, value = dense<[]> : tensor<0xf32>} : () -> tensor<0xf32>

  // Get test parameters
  auto& test_params = GetParam();
  DataType dtype = std::get<0>(test_params);
  TensorShape shape(std::get<1>(test_params));
  bool tensorproto_use_tensor_content = std::get<2>(test_params);

  // Construct a Tensor with the given dtype + shape
  Tensor expected(dtype, shape);
  testing::FillNumericTensorBuffer(expected.dtype(), expected.NumElements(),
                                   expected.data(), 42);

  tensorflow::PartialTensorShape unknown_shape;
  TensorSpec ts1;
  ts1.shape = unknown_shape;
  ts1.dtype = tensorflow::DT_FLOAT;

  TensorSpec ts2;
  ts2.shape = tensorflow::PartialTensorShape({2});
  ts2.dtype = tensorflow::DT_FLOAT;

  TensorSpec ts3;
  ts3.shape = tensorflow::PartialTensorShape({1, 2});
  ts3.dtype = tensorflow::DT_FLOAT;

  EXPECT_TRUE(absl::VerifyTypeImplementsAbslHashCorrectly({ts1, ts2, ts3}));
}

			CheckSize(typ)
		}
		return typ
	}

	for _, s := range &basicTypes {
		Types[s.etype] = defBasic(s.etype, BuiltinPkg, s.name)
	}

	for _, s := range &typedefs {
		sameas := s.sameas32
		if PtrSize == 8 {
			sameas = s.sameas64
		}
		SimType[s.etype] = sameas

		Types[s.etype] = defBasic(s.etype, BuiltinPkg, s.name)
	}

	// We create separate byte and rune types for better error messages

    key: "dtype"
    value {
      type: DT_INT32
    }
  }
}
node {
  name: "input1"
  op: "Placeholder"
  attr {
    key: "dtype"
    value {
      type: DT_INT32
    }
  }
}
versions {
  producer: 27
}

# CHECK-LABEL: func @main(%arg0: tensor<4xi32>, %arg1: tensor<4xi32>) -> tensor<*xi32>
# CHECK: attributes {tf.entry_function = {control_outputs = "", inputs = "input0,input1", outputs = "output"}} {

// license that can be found in the LICENSE file.

package os

import (
	"syscall"
	"unsafe"
)

const _P_PID = 0

func wait6(idtype, id, options int) (status int, errno syscall.Errno) {
	var status32 int32 // C.int
	_, _, errno = syscall.Syscall6(syscall.SYS_WAIT6, uintptr(idtype), uintptr(id), uintptr(unsafe.Pointer(&status32)), uintptr(options), 0, 0)
	return int(status32), errno

      tensorflow::PartialTensorShape* shape) const = 0;

  // Returns tensor (full) type.
  // While there is no immediate plan to deprecate dtype and shape in favor
  // of only using full type type information, this is a future possibility.
  //
  // Note that map_dtype_to_child_of_tensor() from core/framework/types.h
  // can be used to set a FullTypeDef based on dtype in a derived class if
  // appropriate.

// RUN: odml-to-stablehlo-opt --compose-uniform-quantized-type \
// RUN:     --split-input-file --verify-diagnostics %s | FileCheck %s

module {
// CHECK-LABEL: quantized_conv_op
// CHECK-SAME: %[[ARG:.*]]: tensor<1x3x3x4xf32>
  func.func @quantized_conv_op(%arg0: tensor<1x3x3x4xf32>) -> tensor<1x3x3x4xf32> {
    %1 = stablehlo.constant dense<1.000000e+03> : tensor<1x1x1x1xf32>  // Input inverse scale.