code: def convert_to_date_tensor(date_inputs):
      """Converts supplied data to a `DateTensor` if possible.
  
      Args:
        date_inputs: One of the supported types that can be converted to a
          DateTensor. The following input formats are supported. 1. Sequence of
          `datetime.datetime`, `datetime.date`, or any other structure with data
          attributes called 'year', 'month' and 'day'. 2. A numpy array of
          `datetime64` type. 3. Sequence of (year, month, day) Tuples. Months are
          1-based (with January as 1) and constants.Months enum may be used instead
          of ints. Days are also 1-based. 4. A tuple of three int32 `Tensor`s
          containing year, month and date as positive integers in that order. 5. A
          single int32 `Tensor` containing ordinals (i.e. number of days since 31
          Dec 0 with 1 being 1 Jan 1.)
  
      Returns:
        A `DateTensor` object representing the supplied dates.
  
      Raises:
        ValueError: If conversion fails for any reason.
      """
      if isinstance(date_inputs, DateTensor):
          return date_inputs
  
      if hasattr(date_inputs, "year"):  # Case 1.
          return from_datetimes(date_inputs)
  
      if isinstance(date_inputs, np.ndarray):  # Case 2.
          date_inputs = date_inputs.astype("datetime64[D]")
          return from_np_datetimes(date_inputs)
  
      if tf.is_tensor(date_inputs):  # Case 5
          return from_ordinals(date_inputs)
  
      if isinstance(date_inputs, collections.abc.Sequence):
          if not date_inputs:
              return from_ordinals([])
          test_element = date_inputs[0]
          if hasattr(test_element, "year"):  # Case 1.
              return from_datetimes(date_inputs)
          # Case 3
          if isinstance(test_element, collections.abc.Sequence):
              return from_tuples(date_inputs)
          if len(date_inputs) == 3:  # Case 4.
              return from_year_month_day(
                  date_inputs[0], date_inputs[1], date_inputs[2]
              )
      # As a last ditch effort, try to convert the sequence to a Tensor to see if
      # that can work
      try:
          as_ordinals = tf.convert_to_tensor(
              date_inputs, dtype=tf.int32
          )
          return from_ordinals(as_ordinals)
      except ValueError as e:
          raise ValueError(
              "Failed to convert inputs to DateTensor. "
              "Unrecognized format. Error: " + e
          )
  
imports: import collections.abc
  import datetime
  import numpy
  import tensorflow.compat.v2
  from tf_quant_finance.datetime import constants
  from tf_quant_finance.datetime import date_utils
  from tf_quant_finance.datetime import periods
  from tf_quant_finance.datetime import tensor_wrapper
label_desc: Use image node_id 2 for calling a global function with example usage: convert_to_date_tensor(date_inputs) and returns: date_inputs, from_datetimes, from_np_datetimes, from_ordinals, from_ordinals, from_ordinals, from_datetimes, from_tuples, from_year_month_day