class_docstr: Generator of handwritten digits similar to the ones in the MNIST dataset.
class_docstr_tok: ["Generator","of","handwritten","digits","similar","to","the","ones","in","the","MNIST","dataset","."]
docstr: Return a batch of generated images.
  Users should invoke __call__ to run the network, which delegates to this
  method (and not call this method directly).
  Args:
    inputs: A batch of noise vectors as a Tensor with shape
      [batch_size, length of noise vectors].
  Returns:
    A Tensor containing generated images. If data_format is 'channels_last',
    the shape of returned images is [batch_size, 28, 28, 1], else
    [batch_size, 1, 28, 28]
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code: def call(self, inputs):
      """Return a batch of generated images.
      Users should invoke __call__ to run the network, which delegates to this
      method (and not call this method directly).
      Args:
        inputs: A batch of noise vectors as a Tensor with shape
          [batch_size, length of noise vectors].
      Returns:
        A Tensor containing generated images. If data_format is 'channels_last',
        the shape of returned images is [batch_size, 28, 28, 1], else
        [batch_size, 1, 28, 28]
      """
  
      x = self.fc1(inputs)
      x = tf.reshape(x, shape=self._pre_conv_shape)
      x = self.conv1(x)
      x = self.conv2(x)
      return x
  
imports: from __future__ import absolute_import, division, print_function
  import argparse
  import os
  import sys
  import time
  from tempfile import gettempdir
  import tensorflow
  from tensorflow.examples.tutorials.mnist import input_data
  from clearml import Task
label_desc: Use image node_id 2 for calling the Generator obj's underlying member method code with example usage: obj.call(inputs) and returns: x