class_docstr: Prints the value of some tensors in each step.
  It's an example of how ``before_run/after_run`` works.
class_docstr_tok: ["Prints","the","value","of","some","tensors","in","each","step",".","It","'s","an","example","of","how","``","before_run\/after_run","``","works","."]
docstr: Args:
      names(list): list of string, the names of the tensors to print.
docstr_tok: ["Args",":","names","(","list",")",":","list","of","string",",","the","names","of","the","tensors","to","print","."]
code: def __init__(self, names):
      """
      Args:
          names(list): list of string, the names of the tensors to print.
      """
      names = [get_op_tensor_name(n)[1] for n in names]
      logger.warn(
          "Using tf.Print in the graph is much faster than TensorPrinter!"
      )
      self._names = names
  
imports: import tqdm
  from six.moves import zip
  from ..compat import tfv1
  from ..tfutils.common import get_global_step_var, get_op_tensor_name
  from ..utils import logger
  from ..utils.naming import GLOBAL_STEP_INCR_OP_NAME
  from ..utils.utils import get_tqdm_kwargs
  from .base import Callback
label_desc: Use image node_id 1 to create a new TensorPrinter object from inherited base classes: Callback with example: obj = TensorPrinter(names)