code: def softmax(x):
      """Softmax.
  
      Computes softmax activations.
  
      For each batch `i` and class `j` we have
  
        softmax[i, j] = exp(logits[i, j]) / sum(exp(logits[i]))
  
      Arguments:
          x: A `Tensor`. Must be one of the following types: `float32`,
              `float64`. 2-D with shape `[batch_size, num_classes]`.
  
      Returns:
          A `Tensor`. Has the same type as `x`. Same shape as `x`.
      """
      return tf.nn.softmax(x)
  
imports: from __future__ import division, print_function, absolute_import
  import tensorflow.compat.v1
  import numpy
  import tflearn
  from .None import initializations
  from .None import variables
  from .utils import get_from_module
label_desc: Use image node_id 5 for calling a global function with example usage: softmax(x) and returns: tf