// wbMove performs the write barrier operations necessary before
// copying a region of memory from src to dst of type typ.
// Does not actually do the copying.
//
//go:nowritebarrierrec
//go:nosplit
func wbMove(typ *_type, dst, src unsafe.Pointer) {
	// This always copies a full value of type typ so it's safe to
	// pass a type here.
	//
	// See the comment on bulkBarrierPreWrite.

          size: 3
        }
      }
    }
  }
}
node {
  name: "MobilenetV1/Conv2d_0/weights"
  op: "Const"
  attr {
    key: "dtype"
    value {
      type: DT_FLOAT
    }
  }
  attr {
    key: "value"
    value {
      tensor {
        dtype: DT_FLOAT
        tensor_shape {
          dim {
            size: 3
          }
          dim {
            size: 3
          }

        // Identity node that is forwarding the value of a frozen
        // VarhandleOp. We ensure that the dtype matches of the variable dtype.
        NodeDef* new_node = frozen_graph_def->add_node();
        *new_node = node;
        (*new_node->mutable_attr())["T"] =
            name_to_node_map.at(*handle_name)->attr().at("dtype");
        continue;
      }
    }
    // If the node isn't a variable, just copy the node as-is.

    AllocatorAttributes attr;
    if (cc_input.dtype() == ::tensorflow::DT_VARIANT) {
      attr.set_on_host(true);
    }
    TF_RETURN_IF_ERROR(cc_ctx->allocate_temp(cc_input.dtype(), cc_input.shape(),
                                             cc_out, attr));

    switch (cc_input.dtype()) {
      case ::tensorflow::DT_INVALID: {
        *cc_out = Tensor(::tensorflow::DT_INVALID);

    fun createConversion(type: KType): HLParameterConversion {
        val nullable = type.isMarkedNullable
        val kClass = type.classifier as KClass<*>
        return tryMapAllowedType(kClass)
            ?: tryMapPsiElementType(kClass)
            ?: tryMapFirTypeToKtType(kClass, nullable)
            ?: tryMapPlatformType(type, kClass)
            ?: error("Unsupported type $type, consider add corresponding mapping")
    }

  }
  strings::StrAppend(&ret, "}");
  return ret;
}

string PrintTensor(const TensorProto& tensor_proto) {
  Tensor t(tensor_proto.dtype());
  CHECK(t.FromProto(tensor_proto));
  const int64_t num_elts = t.NumElements();
  switch (t.dtype()) {
    case DT_FLOAT:
      return PrintArray(num_elts, t.flat<float>().data());
    case DT_DOUBLE:
      return PrintArray(num_elts, t.flat<double>().data());

        absl::StrSplit(type, absl::ByAnyChar("()"));
    // If type has subtypes then parts[0] = type, parts[1] = subtypes,
    // parts[2] = ""
    if (parts.size() != 3 && parts.size() != 1) {
      return errors::InvalidArgument("Invalid type '", type, "'");
    } else if (parts.size() == 3) {
      // First part is the type, second is the subtype
      ArrayInfo::SubTypeInfo subtype;

type Error interface {
	error

	// RuntimeError is a no-op function but
	// serves to distinguish types that are run time
	// errors from ordinary errors: a type is a
	// run time error if it has a RuntimeError method.
	RuntimeError()
}

// A TypeAssertionError explains a failed type assertion.
type TypeAssertionError struct {
	_interface    *_type
	concrete      *_type

                               tensorflow::CustomDevice* device,
                               tensorflow::DataType dtype, void* data,
                               TFE_CustomDeviceTensorHandleMethods methods)
      : tensorflow::CustomDeviceTensorHandle(context, device, dtype),
        data_(data),
        methods_(methods) {}

  ~CAPICustomDeviceTensorHandle() override { methods_.deallocator(data_); }

          DataType dtype = n->output_type(0);
          AddNodeAttr("dtype", dtype, &const_def);
          TensorProto value;
          value.set_dtype(dtype);
          value.mutable_tensor_shape()->add_dim()->set_size(
              shape_proto.dim_size());
          for (const auto& dim : shape_proto.dim()) {
            if (dtype == DT_INT32) {
              value.add_int_val(dim.size());