class_docstr: Adam with SGD updates every n steps with accumulated gradients.
class_docstr_tok: ["Adam","with","SGD","updates","every","n","steps","with","accumulated","gradients","."]
code: def _resource_apply_sparse_duplicate_indices(
      self, grad, var, indices
  ):
      tf.logging.warning(
          "MultistepAdamOptimizer does not support sparse updates"
      )
      # Note that conversion to a dense Tensor handles duplicate `indices`
      # correctly (summing them). A real sparse implementation will probably want
      # to override _resource_apply_sparse instead so it gets them de-duplicated
      # automatically.
      dense_grad = tf.convert_to_tensor(
          tf.IndexedSlices(
              values=grad, indices=indices, dense_shape=tf.shape(var)
          )
      )
      return self._apply_cond(
          self._resource_apply_dense_in_action, dense_grad, var
      )
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import tensorflow.compat.v1
  from tensorflow.python.ops import resource_variable_ops
  from tensorflow.python.training import training_ops
label_desc: Use image node_id 13 for calling the MultistepAdamOptimizer obj's underlying member method code with example usage: obj._resource_apply_sparse_duplicate_indices(grad, var, indices) and returns: self