docstr: Run session for predicting with new batch of data(Input)
  
  Parameters
  ----------
  feed_dict: Dictionary
      Dictionary to store a batch of input data.
  
  Returns
  -------
  output: TensorFlow Tensor
      Feature map from building the network.
docstr_tok: ["Run","session","for","predicting","with","new","batch","of","data","(","Input",")","Parameters","--","--","--","--","--","feed_dict",":","Dictionary","Dictionary","to","store","a","batch","of","input","data",".","Returns","--","--","--","-","output",":","TensorFlow","Tensor","Feature","map","from","building","the","network","."]
code: def predict(self, feed_dict):
      """
      Run session for predicting with new batch of data(Input)
  
      Parameters
      ----------
      feed_dict: Dictionary
          Dictionary to store a batch of input data.
  
      Returns
      -------
      output: TensorFlow Tensor
          Feature map from building the network.
      """
      for key in feed_dict.keys():
          feed_dict[key] = _utils.convert_shared_float_array_to_numpy(
              feed_dict[key]
          )
  
      output = self.sess.run(
          [self.tf_model],
          feed_dict={
              self.images: feed_dict["input"],
              self.is_train: False,
          },
      )
  
      # TODO: Include self.labels: feed_dict['label'] to handle labels from validation set
      result = {}
      result["output"] = _np.array(output[0])
      return result
  
imports: from __future__ import print_function
  from __future__ import division
  from __future__ import absolute_import
  import numpy
  from .._tf_model import TensorFlowModel
  from turicreate._deps.minimal_package import _minimal_package_import_check
  import turicreate.toolkits._tf_utils
label_desc: Use image node_id 10 for calling the ODTensorFlowModel obj's underlying member method code with example usage: obj.predict(feed_dict) and returns: result