Returns:
          A map of: output key -> output result.
        """
        out = math_ops.matmul(input_tensor, self.filters, name='sample/matmul')

        if self.same_scale_op == 'concatenate':
          ones = array_ops.ones_like(out)
          out = array_ops.concat([out, ones], 0)
        elif self.same_scale_op == 'gather':
          out = array_ops.gather(out, indices=[0], axis=0)

	s.err = err
	return s
}

// Code returns code of the Status.
func (s *Status) Code() Code {
	if s == nil {
		return Success
	}
	return s.code
}

// Message returns a concatenated message on reasons of the Status.
func (s *Status) Message() string {
	if s == nil {
		return ""
	}
	return strings.Join(s.Reasons(), ", ")
}

// SetPlugin sets the given plugin name to s.plugin.

		}
		// otherwise assume a function instead
	}
	return f.Name.Name
}

// separator is an empty //-style comment that is interspersed between
// different comment groups when they are concatenated into a single group
var separator = &Comment{token.NoPos, "//"}

// MergePackageFiles creates a file AST by merging the ASTs of the
// files belonging to a package. The mode flags control merging behavior.

    return %6 : tensor<2x2xf32>
  }

  // CHECK-LABEL: func @concat_v2
  func.func @concat_v2(%arg0: tensor<3x3xf32>, %arg1: tensor<3x3xf32>) -> tensor<6x3xf32> {
    // CHECK: "mhlo.concatenate"({{.*}}) <{dimension = 0 : i64}> : (tensor<3x3xf32>, tensor<3x3xf32>) -> tensor<6x3xf32>
    %axis = "tf.Const"() { value = dense<0> : tensor<i64> } : () -> tensor<i64>

    tf_device::ParallelExecuteOp* new_parallel_execute,
    int* cluster_idx) {
  const int num_cores_per_replica = tpu_devices.front().size();
  // parallel_execute op returns concatenated list of return values of
  // all its regions.
  //
  // TODO(b/149102702): Correctly map inputs to parallel_execute op via
  // identifying xla_sharding op in the cluster_func function.

}

var errUnrecognized = fmt.Errorf("unrecognized profile format")
var errMalformed = fmt.Errorf("malformed profile format")
var errNoData = fmt.Errorf("empty input file")
var errConcatProfile = fmt.Errorf("concatenated profiles detected")

func parseLegacy(data []byte) (*Profile, error) {
	parsers := []func([]byte) (*Profile, error){
		parseCPU,
		parseHeap,
		parseGoCount, // goroutine, threadcreate
		parseThread,

  // CHECK: [[CONSTANT2:%.*]] = mhlo.constant dense<16> : tensor<1xi64>
  // CHECK: [[CONCATENATE:%.*]] = "mhlo.concatenate"([[CONSTANT1]], [[FROM_ELEMENTS]], [[CONSTANT2]]) <{dimension = 0 : i64}> : (tensor<1xi64>, tensor<1xi64>, tensor<1xi64>) -> tensor<3xi64>
  // CHECK: [[DYNAMIC_GATHER:%.*]] = "mhlo.dynamic_gather"([[INPUT]], [[WHILE_OUT]]#2, [[CONCATENATE]])
  // CHECK-SAME:   dimension_numbers =
  // CHECK-SAME:     offset_dims = [1, 2]

    for (auto result_type : region_terminator->getOperandTypes()) {
      if (result_type !=
          op.getOperation()->getResult(output_index++).getType()) {
        return op.emitOpError() << "output types must be a concatenated "
                                << "list of output types for each regions.";
      }
    }
  }

  // Check that total number of outputs from regions match the output types of
  // the parallel_execute op.

// %4 = stablehlo.get_dimension_size %3, dim = 0 :
//          (tensor<?x3x4x2xf32>) -> tensor<i32>
// %5 = stablehlo.reshape %4 :
//          (tensor<i32>) -> tensor<1xi32>
// %6 = stablehlo.concatenate %5, %0, %1, %2, dim = 0 :
//          (tensor<1xi32>, tensor<1xi32>, tensor<1xi32>, tensor<1xi32>)
//            -> tensor<4xi32>
// %7 = stablehlo.dynamic_broadcast_in_dim %arg2, %6
// %8 = stablehlo.add %3, %7
// ```

			klog.Fatalf("Failed to initialize CSINode after retrying: %v", err)
		}
	}()
	return nil
}

func (p *csiPlugin) GetPluginName() string {
	return CSIPluginName
}

// GetvolumeName returns a concatenated string of CSIVolumeSource.Driver<volNameSe>CSIVolumeSource.VolumeHandle
// That string value is used in Detach() to extract driver name and volumeName.
func (p *csiPlugin) GetVolumeName(spec *volume.Spec) (string, error) {