code: def sampled_softmax_loss_v2(
      weights,
      biases,
      labels,
      inputs,
      num_sampled,
      num_classes,
      num_true=1,
      sampled_values=None,
      remove_accidental_hits=True,
      seed=None,
      name="sampled_softmax_loss",
  ):
      """Computes and returns the sampled softmax training loss.
  
      This is a faster way to train a softmax classifier over a huge number of
      classes.
  
      This operation is for training only.  It is generally an underestimate of
      the full softmax loss.
  
      A common use case is to use this method for training, and calculate the full
      softmax loss for evaluation or inference as in the following example:
  
      ```python
      if mode == "train":
        loss = tf.nn.sampled_softmax_loss(
            weights=weights,
            biases=biases,
            labels=labels,
            inputs=inputs,
            ...)
      elif mode == "eval":
        logits = tf.matmul(inputs, tf.transpose(weights))
        logits = tf.nn.bias_add(logits, biases)
        labels_one_hot = tf.one_hot(labels, n_classes)
        loss = tf.nn.softmax_cross_entropy_with_logits(
            labels=labels_one_hot,
            logits=logits)
      ```
  
      See our [Candidate Sampling Algorithms Reference]
      (https://www.tensorflow.org/extras/candidate_sampling.pdf)
  
      Also see Section 3 of [Jean et al., 2014](http://arxiv.org/abs/1412.2007)
      ([pdf](http://arxiv.org/pdf/1412.2007.pdf)) for the math.
  
      Note: when doing embedding lookup on `weights` and `bias`, "div" partition
      strategy will be used. Support for other partition strategy will be added
      later.
  
      Args:
        weights: A `Tensor` of shape `[num_classes, dim]`, or a list of `Tensor`
          objects whose concatenation along dimension 0 has shape [num_classes,
          dim].  The (possibly-sharded) class embeddings.
        biases: A `Tensor` of shape `[num_classes]`.  The class biases.
        labels: A `Tensor` of type `int64` and shape `[batch_size, num_true]`. The
          target classes.  Note that this format differs from the `labels` argument
          of `nn.softmax_cross_entropy_with_logits`.
        inputs: A `Tensor` of shape `[batch_size, dim]`.  The forward activations of
          the input network.
        num_sampled: An `int`.  The number of classes to randomly sample per batch.
        num_classes: An `int`. The number of possible classes.
        num_true: An `int`.  The number of target classes per training example.
        sampled_values: a tuple of (`sampled_candidates`, `true_expected_count`,
          `sampled_expected_count`) returned by a `*_candidate_sampler` function.
          (if None, we default to `log_uniform_candidate_sampler`)
        remove_accidental_hits:  A `bool`.  whether to remove "accidental hits"
          where a sampled class equals one of the target classes.  Default is True.
        seed: random seed for candidate sampling. Default to None, which doesn't set
          the op-level random seed for candidate sampling.
        name: A name for the operation (optional).
  
      Returns:
        A `batch_size` 1-D tensor of per-example sampled softmax losses.
  
      """
      return sampled_softmax_loss(
          weights,
          biases,
          labels,
          inputs,
          num_sampled,
          num_classes,
          num_true=num_true,
          sampled_values=sampled_values,
          remove_accidental_hits=remove_accidental_hits,
          partition_strategy="div",
          name=name,
          seed=seed,
      )
  
imports: import math
  from tensorflow.python.framework import constant_op
  from tensorflow.python.framework import dtypes
  from tensorflow.python.framework import ops
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import array_ops_stack
  from tensorflow.python.ops import candidate_sampling_ops
  from tensorflow.python.ops import cond
  from tensorflow.python.ops import ctc_ops
  from tensorflow.python.ops import custom_gradient
  from tensorflow.python.ops import embedding_ops
  from tensorflow.python.ops import gen_nn_ops
  from tensorflow.python.ops import gen_sparse_ops
  from tensorflow.python.ops import linalg_ops
  from tensorflow.python.ops import math_ops
  from tensorflow.python.ops import nn_fused_batch_norm_grad
  from tensorflow.python.ops import nn_ops
  from tensorflow.python.ops import variables
  from tensorflow.python.platform import device_context
  from tensorflow.python.util import dispatch
  from tensorflow.python.util.deprecation import deprecated_args
  from tensorflow.python.util.deprecation import deprecated_argument_lookup
  from tensorflow.python.util.tf_export import tf_export
label_desc: Use image node_id 31 for calling a global function with example usage: sampled_softmax_loss_v2(weights, biases, labels, inputs, num_sampled, num_classes, num_true, sampled_values, remove_accidental_hits, seed, name) and returns: sampled_softmax_loss