code: def _make_distribution(
      self, dist_name, params, batch_shape, override_params=None
  ):
      override_params = override_params or {}
      all_params = dict(params)
      for param_name, override_param in override_params.items():
          all_params[param_name] = override_param
      all_params = dhps.constrain_params(all_params, dist_name)
      all_params = dhps.modify_params(
          all_params, dist_name, validate_args=False
      )
      return dhps.base_distributions(
          enable_vars=False,
          dist_name=dist_name,
          params=all_params,
          batch_shape=batch_shape,
          validate_args=False,
      )
  
imports: import functools
  import os
  from absl import flags
  from absl.testing import parameterized
  import hypothesis
  from hypothesis import strategies
  import jax
  from jax import random
  import jax.numpy
  from tensorflow_probability.python.internal import reparameterization
  from tensorflow_probability.python.internal.backend import jax
  from tensorflow_probability.substrates.jax.bijectors import bijector_test_util
  from tensorflow_probability.substrates.jax.distributions import hypothesis_testlib
  from tensorflow_probability.substrates.jax.distributions import normal
  from tensorflow_probability.substrates.jax.distributions import transformed_distribution
  from tensorflow_probability.substrates.jax.internal import hypothesis_testlib
  from tensorflow_probability.substrates.jax.internal import test_util
label_desc: Use image node_id 1 for calling the _GradTest obj's underlying member method code with example usage: obj._make_distribution(dist_name, params, batch_shape, override_params) and returns: dhps