code: def quadrature_scheme_lognormal_gauss_hermite(
      loc, scale, quadrature_size, validate_args=False, name=None
  ):  # pylint: disable=unused-argument
      """Use Gauss-Hermite quadrature to form quadrature on positive-reals.
  
      Note: for a given `quadrature_size`, this method is generally less accurate
      than `quadrature_scheme_lognormal_quantiles`.
  
      Args:
        loc: `float`-like (batch of) scalar `Tensor`; the location parameter of
          the LogNormal prior.
        scale: `float`-like (batch of) scalar `Tensor`; the scale parameter of
          the LogNormal prior.
        quadrature_size: Python `int` scalar representing the number of quadrature
          points.
        validate_args: Python `bool`, default `False`. When `True` distribution
          parameters are checked for validity despite possibly degrading runtime
          performance. When `False` invalid inputs may silently render incorrect
          outputs.
        name: Python `str` name prefixed to Ops created by this class.
  
      Returns:
        grid: (Batch of) length-`quadrature_size` vectors representing the
          `log_rate` parameters of a `Poisson`.
        probs: (Batch of) length-`quadrature_size` vectors representing the
          weight associate with each `grid` value.
      """
      with tf.name_scope(
          name or "vector_diffeomixture_quadrature_gauss_hermite"
      ):
          grid, probs = np.polynomial.hermite.hermgauss(
              deg=quadrature_size
          )
          npdt = dtype_util.as_numpy_dtype(loc.dtype)
          grid = grid.astype(npdt)
          probs = probs.astype(npdt)
          probs /= np.linalg.norm(probs, ord=1, keepdims=True)
          probs = tf.convert_to_tensor(
              probs, name="probs", dtype=loc.dtype
          )
          # The following maps the broadcast of `loc` and `scale` to each grid
          # point, i.e., we are creating several log-rates that correspond to the
          # different Gauss-Hermite quadrature points and (possible) batches of
          # `loc` and `scale`.
          grid = (
              loc[..., tf.newaxis]
              + np.sqrt(2.0) * scale[..., tf.newaxis] * grid
          )
          return grid, probs
  
imports: import numpy
  import tensorflow.compat.v2
  from tensorflow_probability.python.bijectors import exp
  from tensorflow_probability.python.distributions import categorical
  from tensorflow_probability.python.distributions import distribution
  from tensorflow_probability.python.distributions import normal
  from tensorflow_probability.python.distributions import poisson
  from tensorflow_probability.python.distributions import transformed_distribution
  from tensorflow_probability.python.internal import assert_util
  from tensorflow_probability.python.internal import distribution_util
  from tensorflow_probability.python.internal import dtype_util
  from tensorflow_probability.python.internal import prefer_static
  from tensorflow_probability.python.internal import reparameterization
  from tensorflow_probability.python.internal import samplers
  from tensorflow_probability.python.internal import tensor_util
  from tensorflow_probability.python.internal import tensorshape_util
label_desc: Use image node_id 1 for calling a global function with example usage: quadrature_scheme_lognormal_gauss_hermite(loc, scale, quadrature_size, validate_args, name) and returns: grid,  probs