docstr: Run session for training with new batch of data(Input and Label)
  
  Parameters
  ----------
  feed_dict: Dictionary
      Dictionary to store a batch of input data, corresponding labels and iteration number. This is currently
      passed from the object_detector.py file when a new batch of data is sent.
  
  Returns
  -------
  loss_batch: TensorFlow Tensor
      Loss per batch
docstr_tok: ["Run","session","for","training","with","new","batch","of","data","(","Input","and","Label",")","Parameters","--","--","--","--","--","feed_dict",":","Dictionary","Dictionary","to","store","a","batch","of","input","data",",","corresponding","labels","and","iteration","number",".","This","is","currently","passed","from","the","object_detector.py","file","when","a","new","batch","of","data","is","sent",".","Returns","--","--","--","-","loss_batch",":","TensorFlow","Tensor","Loss","per","batch"]
code: def train(self, feed_dict):
      """
      Run session for training with new batch of data(Input and Label)
  
      Parameters
      ----------
      feed_dict: Dictionary
          Dictionary to store a batch of input data, corresponding labels and iteration number. This is currently
          passed from the object_detector.py file when a new batch of data is sent.
  
      Returns
      -------
      loss_batch: TensorFlow Tensor
          Loss per batch
      """
      for key in feed_dict.keys():
          feed_dict[key] = _utils.convert_shared_float_array_to_numpy(
              feed_dict[key]
          )
      feed_dict["labels"] = feed_dict["labels"].reshape(
          self.batch_size,
          self.grid_shape[0],
          self.grid_shape[1],
          self.num_anchors,
          self.num_classes + 5,
      )
  
      _, loss_batch = self.sess.run(
          [self.train_op, self.loss],
          feed_dict={
              self.images: feed_dict["input"],
              self.labels: feed_dict["labels"],
              self.is_train: True,
          },
      )
      result = {}
      result["loss"] = _np.array([loss_batch])
      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 9 for calling the ODTensorFlowModel obj's underlying member method code with example usage: obj.train(feed_dict) and returns: result