code: def get_data(n, clip_norm):
      # Data is for hidden weights of w* = [3, 1] and bias of b* = 2.
      # For mean-squared loss, we have:
      # loss = (data * w + b - label)^2 = (data * (w - w*) + (b - b*))^2
      # Let T = (data * (w - w*) + (b - b*)), we have:
      #   grad_w = 2 * T * data
      #   grad_b = 2 * T
      # For w = [0, 0], b = 0:
      #   For data = [3, 4],  T = -15, grad_w = [-90, -120],  grad_b = -30
      #   For data = [1, -1], T = -4,  grad_w = [-8, 8],      grad_b = -8
      data = np.array([[3.0, 4.0], [1.0, -1.0]])
      labels = np.matmul(data, [[3], [1]]) + 2
      data, labels = np.tile(data, (n, 1)), np.tile(labels, (n, 1))
  
      def clip_grad(grad):
          norm = np.linalg.norm(grad)
          if norm <= clip_norm:
              return grad
          return grad / norm * clip_norm
  
      grad1 = clip_grad(np.array([-90.0, -120.0, -30.0]))
      grad2 = clip_grad(np.array([-8.0, 8.0, -8.0]))
      grad = np.mean(np.vstack([grad1, grad2]), axis=0)
      return data, labels, grad
  
file_path: privacy/tensorflow_privacy/privacy/keras_models/dp_keras_model_distributed_test.py
imports: import contextlib
  import multiprocessing
  import os
  import sys
  from absl.testing import parameterized
  import numpy
  import tensorflow
  from tensorflow_privacy.privacy.fast_gradient_clipping import layer_registry
  from tensorflow_privacy.privacy.keras_models import dp_keras_model
label_desc: Use image node_id 1 for calling a global function with example usage: get_data(n, clip_norm) and returns: data,  labels,  grad, unknown, grad