code: def _make_dataset(
      self,
      n,
      d,
      link,
      scale=1.0,
      batch_shape=None,
      dtype=np.float32,
      seed=42,
  ):
      seed = test_util.test_seed_stream(
          hardcoded_seed=seed, salt="tfp.glm.proximal_hessian_test"
      )
  
      if batch_shape is None:
          batch_shape = []
  
      model_coefficients = uniform.Uniform(
          low=np.array(-1, dtype), high=np.array(1, dtype)
      ).sample(batch_shape + [d], seed=seed())
  
      radius = tf.cast(np.sqrt(2.0), dtype)
      model_coefficients *= (
          radius
          / tf.linalg.norm(tensor=model_coefficients, axis=-1)[
              ..., tf.newaxis
          ]
      )
  
      mask = bernoulli.Bernoulli(probs=0.5, dtype=tf.bool).sample(
          batch_shape + [d], seed=seed()
      )
      model_coefficients = tf.where(
          mask, model_coefficients, tf.zeros_like(model_coefficients)
      )
      model_matrix = normal.Normal(
          loc=np.array(0, dtype), scale=np.array(1, dtype)
      ).sample(batch_shape + [n, d], seed=seed())
      scale = tf.convert_to_tensor(value=scale, dtype=dtype)
      linear_response = tf.matmul(
          model_matrix, model_coefficients[..., tf.newaxis]
      )[..., 0]
  
      if link == "linear":
          response = normal.Normal(
              loc=linear_response, scale=scale
          ).sample(seed=seed())
      elif link == "probit":
          response = tf.cast(
              normal.Normal(loc=linear_response, scale=scale).sample(
                  seed=seed()
              )
              > 0,
              dtype,
          )
      elif link == "logit":
          response = bernoulli.Bernoulli(logits=linear_response).sample(
              seed=seed()
          )
      else:
          raise ValueError("unrecognized true link: {}".format(link))
      return self.evaluate(
          [model_matrix, response, model_coefficients, mask]
      )
  
imports: import numpy
  import tensorflow.compat.v1
  import tensorflow.compat.v2
  from tensorflow_probability.python.distributions import bernoulli
  from tensorflow_probability.python.distributions import normal
  from tensorflow_probability.python.distributions import uniform
  from tensorflow_probability.python.glm import family
  from tensorflow_probability.python.glm import proximal_hessian
  from tensorflow_probability.python.internal import test_util
  from tensorflow_probability.python.math import sparse
label_desc: Use image node_id 1 for calling the _ProximalHessianTest obj's underlying member method code with example usage: obj._make_dataset(n, d, link, scale, batch_shape, dtype, seed) and returns: self