code: def make_dataset(self, n, d, link, offset=None, scale=1.0):
      seed = test_util.test_seed_stream(
          hardcoded_seed=213356351, salt="tfp.glm.fisher_scoring_test"
      )
      model_coefficients = uniform.Uniform(
          low=np.array(-0.5, self.dtype), high=np.array(0.5, self.dtype)
      ).sample(d, seed=seed())
      radius = tf.cast(np.sqrt(2.0), self.dtype)
      model_coefficients *= radius / tf.linalg.norm(
          tensor=model_coefficients
      )
      model_matrix = normal.Normal(
          loc=np.array(0, self.dtype), scale=np.array(1, self.dtype)
      ).sample([n, d], seed=seed())
      scale = tf.convert_to_tensor(value=scale, dtype=self.dtype)
      linear_response = tf.tensordot(
          model_matrix, model_coefficients, axes=[[1], [0]]
      )
      if offset is not None:
          linear_response += offset
      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,
              self.dtype,
          )
      elif link == "logit":
          response = bernoulli.Bernoulli(logits=linear_response).sample(
              seed=seed()
          )
      else:
          raise ValueError("unrecognized true link: {}".format(link))
      return model_matrix, response, model_coefficients, linear_response
  
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 fisher_scoring
  from tensorflow_probability.python.internal import dtype_util
  from tensorflow_probability.python.internal import test_util
label_desc: Use image node_id 1 for calling the FitTestFast obj's underlying member method code with example usage: obj.make_dataset(n, d, link, offset, scale) and returns: model_matrix,  response,  model_coefficients,  linear_response