code: def nce_loss(
      weights,
      biases,
      inputs,
      labels,
      noise_distribution,
      num_samples=32,
      allow_duplicates=True,
      seed=auto_select,
      name="",
  ):
      """nce_loss(weights, biases, inputs, labels, noise_distribution, num_samples=32, allow_duplicates=True, seed=auto_select, name='')
      Computes the noise contrastive estimation loss. This implementation mostly
      follows Chris Dyer's notes [1]. At a high level, this layer draws
      `num_samples` random labels from `noise_distribution` and then forms
      `num_samples`+1 binary classification problems where the true label is
      considered a positive example and the random labels are considered negative
      examples. The negatives are shared among all the examples in the
      minibatch. This operation only computes the logits for the labels in the
      minibatch and the random labels drawn from `noise_distribution`. The
      gradients will be sparse if the labels are sparse.
  
      The `noise_distribution` is read once and certain quantities are
      precomputed based on it. This operation will need to be reinstantiated if
      the `noise_distribution` changes.
  
      Shape inference for the weights is currently not supported when inputs are
      placeholders. Either a concrete input must be used or the weights must be
      provided without any inferred dimensions.
  
      Example:
          >>> import scipy
          >>> # dimensions of input, number of noise labels, batch size, number of classes
          >>> xdim = 10
          >>> samples = 32
          >>> batch = 4
          >>> classes = 100
          >>> # some variables; typically x will be the output of a layer
          >>> x = C.input_variable(xdim)
          >>> y = C.input_variable(classes, is_sparse=True)
          >>> # dummy data
          >>> x0 = np.arange(batch * xdim, dtype=np.float32).reshape((batch, xdim))/(batch * xdim)
          >>> data = np.ones(batch, dtype=np.float32)
          >>> indices = list(range(10, 10*batch+1, 10))
          >>> indptr = list(range(batch + 1))
          >>> y0 = scipy.sparse.csr_matrix((data, indices, indptr), shape=(batch, classes))
          >>> # a dummy noise distribution
          >>> q = np.arange(classes, dtype=np.float32) + 1 # normalization not necessary
          >>> # the parameters
          >>> b = C.parameter((classes, 1), init=-np.log(classes))
          >>> W = C.parameter((classes, C.InferredDimension), init=C.glorot_uniform(seed=98052))
          >>> # the loss
          >>> loss = C.nce_loss(W, b, x, y, q, seed=98052)
          >>> # evaluate the loss at our dummy data
          >>> np.round(loss.eval({x:x0, y:y0}), decimals=3)
          array([ 2.385,  3.035,  3.886,  3.868], dtype=float32)
          >>> # after training, use the logits for predictions
          >>> logits = C.times_transpose(x, W) + C.reshape(b, -1)
  
      Args:
          weights: parameter (or variable in general) containing the weights with
           which inputs will be multiplied. Its shape must be
           (number of classes, dimension of input)
          biases: parameter (or variable in general) containing the biases that
           will be added to the product of weights and inputs. Its shape must be
           (number of classes, 1)
          inputs: vector of inputs to this layer. Multiplying by the weights and
           adding the biases gives the logits.
          labels: a one-hot vector with the ground-truth labels.
          noise_distribution: a constant vector with dimension equal to the number
           of classes. The entries must be positive numbers but do not have to
           sum to 1. random labels will be drawn according to the normalized
           distribution.
          num_samples: number of random labels that will be drawn from the
           `noise_distribution`.
          allow_duplicates: boolean. If True (default), the random labels can
           contain duplicates. Compared to `allow_duplicates=False` it is faster
           but the quality of the approximations is slightly worse for the same
           number of samples.
          seed: random seed. The default value selects a unique random seed.
          name (str, optional): the name of the Function instance in the network
      Returns:
          :class:`~cntk.ops.functions.Function`
  
      See also:
          [1] C. Dyer. `Notes on Noise Contrastive Estimation and Negative Sampling [pdf] <http://demo.clab.cs.cmu.edu/cdyer/nce_notes.pdf>`_.
      """
      from cntk.cntk_py import nce_loss
  
      dtype = get_data_type(inputs, labels, noise_distribution)
      inputs = sanitize_input(inputs, dtype)
      labels = sanitize_input(labels, dtype)
      noise_distribution = sanitize_input(noise_distribution, dtype)
      return nce_loss(
          weights,
          biases,
          inputs,
          labels,
          noise_distribution,
          num_samples,
          allow_duplicates,
          seed,
          name,
      )
  
imports: from __future__ import division
  from __future__ import print_function
  import numpy
  from ..ops.functions import CloneMethod, Function
  from ..variables import Variable, Parameter, Constant
  from cntk.internal import sanitize_input, sanitize_shape, sanitize_axis, sanitize_dynamic_axes, typemap
  from cntk.internal.utils import get_data_type
  from cntk.cntk_py import sentinel_value_for_auto_select_random_seed
  from cntk import times, softmax, parameter
  import cntk
  from ..axis import Axis
label_desc: Use image node_id 8 for calling a global function with example usage: nce_loss(weights, biases, inputs, labels, noise_distribution, num_samples, allow_duplicates, seed, name) and returns: nce_loss