chore: import upstream snapshot with attribution

tensorflow/python/data/ops/zip_op.py

@@ -0,0 +1,62 @@
+# Copyright 2022 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""The implementation of `tf.data.Dataset.zip`."""
+
+from tensorflow.python.data.ops import dataset_ops
+from tensorflow.python.data.util import nest
+from tensorflow.python.ops import gen_dataset_ops
+from tensorflow.python.types import data as data_types
+
+
+def _zip(datasets, name):  # pylint: disable=redefined-builtin
+  return _ZipDataset(datasets, name)
+
+
+class _ZipDataset(dataset_ops.DatasetV2):
+  """A `Dataset` that zips its inputs together."""
+
+  def __init__(self, datasets, name=None):
+    """See `Dataset.zip()` for details."""
+    for ds in nest.flatten(datasets):
+      if not isinstance(ds, data_types.DatasetV2):
+        if isinstance(ds, list):
+          raise TypeError(
+              "Invalid input to `zip`. Inputs are expected to be (nested)"
+              " structures of `tf.data.Dataset` objects. Python `list` is"
+              " not supported and you should use `tuple` instead."
+          )
+        else:
+          raise TypeError(
+              "Invalid input to `zip`. Inputs are expected to be (nested)"
+              " structures of `tf.data.Dataset` objects but"
+              f" encountered object of type {type(ds)}."
+          )
+    self._datasets = datasets
+    self._structure = nest.pack_sequence_as(
+        self._datasets, [ds.element_spec for ds in nest.flatten(self._datasets)]
+    )
+    self._name = name
+    variant_tensor = gen_dataset_ops.zip_dataset(
+        [ds._variant_tensor for ds in nest.flatten(self._datasets)],
+        **self._common_args,
+    )
+    super().__init__(variant_tensor)
+
+  def _inputs(self):
+    return nest.flatten(self._datasets)
+
+  @property
+  def element_spec(self):
+    return self._structure