class_docstr: When called evaluates `d(f, args, kwargs)` but supports binding `f`.
  
  >>> @Bind.decorator
  ... def my_decorator(f, args, kwargs):
  ...   print("my_decorator called with", args, kwargs)
  ...   return f(*args, **kwargs)
  
  >>> class Foo:
  ...   @my_decorator
  ...   def bar(self, a, b, c):
  ...     return a * b * c
  
  >>> Foo.bar(None, 1, 2, c=3)
  my_decorator called with (None, 1, 2) {'c': 3}
  6
  
  >>> foo = Foo()
  >>> foo.bar(1, 2, c=3)
  my_decorator called with (1, 2) {'c': 3}
  6
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code: def __call__(self, *a, **k):
      return self._d(self._f, a, k)
  
imports: from tensorflow.python.eager import backprop
  from tensorflow.python.eager import context
  from tensorflow.python.eager import record
  from tensorflow.python.framework import composite_tensor_gradient
  from tensorflow.python.framework import dtypes
  from tensorflow.python.framework import ops
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import gen_array_ops
  from tensorflow.python.ops import handle_data_util
  from tensorflow.python.ops import math_ops
  from tensorflow.python.ops import op_selector
  from tensorflow.python.ops import resource_variable_ops
  from tensorflow.python.ops import variable_scope
  from tensorflow.python.ops.unconnected_gradients import UnconnectedGradients
  from tensorflow.python.platform import tf_logging
  from tensorflow.python.util import nest
  from tensorflow.python.util import tf_decorator
  from tensorflow.python.util import tf_inspect
  from tensorflow.python.util import variable_utils
  from tensorflow.python.util.tf_export import tf_export
label_desc: Use image node_id 4 for calling the Bind obj's underlying member method code with example usage: obj.__call__() and returns: self