code: def call(
      self, x: tf.Tensor, y: tf.Tensor, infer_sigma: bool = False
  ) -> tf.Tensor:
      y = tf.cast(y, x.dtype)
      x, y = tf.reshape(x, (x.shape[0], -1)), tf.reshape(
          y, (y.shape[0], -1)
      )  # flatten
      dist = distance.squared_pairwise_distance(x, y)  # [Nx, Ny]
  
      if infer_sigma or self.init_required:
          if self.trainable and infer_sigma:
              raise ValueError(
                  "Gradients cannot be computed w.r.t. an inferred sigma value"
              )
          sigma = self.init_sigma_fn(x, y, dist)
          self.log_sigma.assign(tf.math.log(sigma))
          self.init_required = False
  
      gamma = tf.constant(
          1.0 / (2.0 * self.sigma**2), dtype=x.dtype
      )  # [Ns,]
      # TODO: do matrix multiplication after all?
      kernel_mat = tf.exp(
          -tf.concat([(g * dist)[None, :, :] for g in gamma], axis=0)
      )  # [Ns, Nx, Ny]
      return tf.reduce_mean(kernel_mat, axis=0)
  
imports: import tensorflow
  import numpy
  from .None import distance
  from typing import Optional, Union, Callable
  from scipy.special import logit
  from alibi_detect.utils.frameworks import Framework
label_desc: Use image node_id 3 for calling the GaussianRBF obj's underlying member method code with example usage: obj.call(x, y, infer_sigma) and returns: tf