attr {
    key: "value"
    value {
      tensor {
        dtype: DT_INT32
        tensor_shape { dim { size: 1 } }
        int_val: 1
      }
    }
  }
  attr { key  : "dtype" value { type: DT_INT32 } }
}
node {
  name  : "y_const"
  op    : "Const"
  attr {
    key: "value"
    value {
      tensor {
        dtype: DT_INT32
        tensor_shape { dim { size: 1 } }
        int_val: 2
      }

  return CreateTensor(buf, dtype, dims, num_dims, len);
}

TF_Tensor* TF_NewTensor(TF_DataType dtype, const int64_t* dims, int num_dims,
                        void* data, size_t len,
                        void (*deallocator)(void* data, size_t len, void* arg),
                        void* deallocator_arg) {
  TF_ManagedBuffer* buf = nullptr;
  if (dtype != TF_STRING && dtype != TF_RESOURCE &&

# Check that we correctly import the node locations.

node {
  name: "node_outside_function"
  op: "Const"
  attr {
    key: "dtype"
    value {
      type: DT_INT32
    }
  }
  attr {
    key: "value"
    value {
      tensor {
        dtype: DT_INT32
        tensor_shape {
        }
        int_val: 0
      }
    }
  }
  experimental_debug_info {
  }
}
node {

          shape: {
            dim: {
              size: 1
            }
          }
          dtype: DT_FLOAT
        }
      }
      values: {
        tensor_spec_value: {
          name : "y"
          shape: {
            dim: {
              size: 1
            }
          }
          dtype: DT_FLOAT
        }
      }
      values: {
        tensor_spec_value: {
          name : "z"

        name: "while_loop_counter"
        type: DT_INT32
      }
      input_arg {
        name: "while_maximum_iterations"
        type: DT_INT32
      }
      input_arg {
        name: "input"
        type: DT_INT32
      }
      output_arg {
        name: "output"
        type: DT_INT32
      }
      output_arg {
        name: "output_1"
        type: DT_INT32
      }
      output_arg {

        VLOG(1) << "[potentially conservative] Op type `" << node.type_string()
                << "` is stateful but effects not modelled";
      } else {
        // See if any resource type is used.
        bool resource = false;
        std::function<bool(mlir::Type)> record_resource;
        record_resource = [&](mlir::Type type) {
          type.walk([&](mlir::Type t) {

node {
  name: "metrics_2/sparse_categorical_accuracy/Const"
  op: "Const"
  device: "/job:localhost/replica:0/task:0/device:GPU:0"
  attr {
    key: "dtype"
    value {
      type: DT_INT32
    }
  }
  attr {
    key: "value"
    value {
      tensor {
        dtype: DT_INT32
        tensor_shape {
          dim {
            size: 1
          }
        }
        int_val: 0
      }
    }
  }
}
node {

  EXPECT_EQ(dltensor_out->device.device_id, dltensor_in->device.device_id);
  EXPECT_EQ(dltensor_out->ndim, dltensor_in->ndim);
  EXPECT_EQ(dltensor_out->dtype.code, dltensor_in->dtype.code);
  EXPECT_EQ(dltensor_out->dtype.bits, dltensor_in->dtype.bits);
  EXPECT_EQ(dltensor_out->dtype.lanes, dltensor_in->dtype.lanes);
  for (int i = 0; i < dltensor_in->ndim; ++i) {
    EXPECT_EQ(dltensor_out->shape[i], dltensor_in->shape[i]);
    if (dltensor_out->strides) {

	}

	for _, tc := range testcases {
		aType := reflect.TypeOf(tc.a)
		bType := reflect.TypeOf(tc.b)
		t.Run(aType.String(), func(t *testing.T) {
			assertEqualTypes(t, nil, aType, bType)
		})
	}
}

func assertEqualTypes(t *testing.T, path []string, a, b reflect.Type) {
	if a == b {
		return
	}

	if a.Kind() != b.Kind() {

        }
      }
    }
  }
}
node {
  name: "BoxPredictor_4/ClassPredictor/weights"
  op: "Const"
  attr {
    key: "dtype"
    value {
      type: DT_FLOAT
    }
  }
  attr {
    key: "value"
    value {
      tensor {
        dtype: DT_FLOAT
        tensor_shape {
          dim {
            size: 1
          }
          dim {
            size: 1
          }