class_docstr: Regression model.
  
  Subclasses must set the following attributes:
    output: Op(s) used for computing regression loss and predicted regression
      outputs for each task.
class_docstr_tok: ["Regression","model",".","Subclasses","must","set","the","following","attributes",":","output",":","Op","(","s",")","used","for","computing","regression","loss","and","predicted","regression","outputs","for","each","task","."]
docstr: Add Placeholders for labels for each task.
  
  This method creates the following Placeholders for each task:
    labels_%d: Label tensor with shape batch_size.
  
  Placeholders are wrapped in identity ops to avoid the error caused by
  feeding and fetching the same tensor.
docstr_tok: ["Add","Placeholders","for","labels","for","each","task",".","This","method","creates","the","following","Placeholders","for","each","task",":","labels_","%","d",":","Label","tensor","with","shape","batch_size",".","Placeholders","are","wrapped","in","identity","ops","to","avoid","the","error","caused","by","feeding","and","fetching","the","same","tensor","."]
code: def add_label_placeholders(self, graph, name_scopes):
      """Add Placeholders for labels for each task.
  
      This method creates the following Placeholders for each task:
        labels_%d: Label tensor with shape batch_size.
  
      Placeholders are wrapped in identity ops to avoid the error caused by
      feeding and fetching the same tensor.
      """
      placeholder_scope = TensorflowGraph.get_placeholder_scope(
          graph, name_scopes
      )
      with graph.as_default():
          batch_size = self.batch_size
          labels = []
          with placeholder_scope:
              for task in range(self.n_tasks):
                  labels.append(
                      tf.identity(
                          tf.placeholder(
                              tf.float32,
                              shape=[None],
                              name="labels_%d" % task,
                          )
                      )
                  )
      return labels
  
imports: from __future__ import print_function
  from __future__ import division
  from __future__ import unicode_literals
  import collections
  import pickle
  import os
  import time
  import warnings
  import numpy
  import pandas
  import tensorflow
  import tempfile
  from deepchem.models import Model
  from deepchem.metrics import from_one_hot
  from deepchem.nn import model_ops
  from deepchem.models.tensorflow_models import utils
  from deepchem.trans import undo_transforms
  from deepchem.utils.save import log
  from deepchem.utils.evaluate import Evaluator
  from deepchem.data import pad_features
  from tensorflow.contrib.layers.python.layers import batch_norm
label_desc: Use image node_id 4 for calling the TensorflowRegressor obj's underlying member method code with example usage: obj.add_label_placeholders(graph, name_scopes) and returns: labels