class_docstr: We need to define a custom keras model here for gradient clipping,
  to stabilize training.
class_docstr_tok: ["We","need","to","define","a","custom","keras","model","here","for","gradient","clipping",",","to","stabilize","training","."]
code: def train_step(self, data):
      # Unpack the data. Its structure depends on your model and
      # on what you pass to `fit()`.
      x, y = data
  
      with tf.GradientTape() as tape:
          y_pred = self(x, training=True)  # Forward pass
          # Compute the loss value
          # (the loss function is configured in `compile()`)
          loss = self.compiled_loss(
              y, y_pred, regularization_losses=self.losses
          )
  
      # Compute gradients
      trainable_vars = self.trainable_variables
      gradients = tape.gradient(loss, trainable_vars)
      gradients = [
          tf.clip_by_value(grad, -4.0, 4.0) for grad in gradients
      ]
      gradients = [
          (tf.where(tf.math.is_nan(grad), tf.zeros_like(grad), grad))
          for grad in gradients
      ]
  
      # Update weights
      self.optimizer.apply_gradients(zip(gradients, trainable_vars))
      # Update metrics (includes the metric that tracks the loss)
      self.compiled_metrics.update_state(y, y_pred)
      # Return a dict mapping metric names to current value
      return {m.name: m.result() for m in self.metrics}
  
imports: import numpy
  from umap import UMAP
  from warnings import warn, catch_warnings, filterwarnings
  from numba import TypingError
  import os
  from umap.spectral import spectral_layout
  from sklearn.utils import check_random_state
  import codecs, pickle
  from sklearn.neighbors import KDTree
  import sys
label_desc: Use image node_id 1 for calling the GradientClippedModel obj's underlying member method code with example usage: obj.train_step(data) and returns: unknown