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diff --git a/tensorflow/models/image/cifar10/cifar10_input.py b/tensorflow/models/image/cifar10/cifar10_input.py
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+"""Routine for decoding the CIFAR-10 binary file format."""
+
+import tensorflow.python.platform
+import tensorflow as tf
+
+
+def read_cifar10(filename_queue):
+  """Reads and parses examples from CIFAR10 data files.
+
+  Recommendation: if you want N-way read parallelism, call this function
+  N times.  This will give you N independent Readers reading different
+  files & positions within those files, which will give better mixing of
+  examples.
+
+  Args:
+    filename_queue: A queue of strings with the filenames to read from.
+
+  Returns:
+    An object representing a single example, with the following fields:
+      height: number of rows in the result (32)
+      width: number of columns in the result (32)
+      depth: number of color channels in the result (3)
+      key: a scalar string Tensor describing the filename & record number
+        for this example.
+      label: an int32 Tensor with the label in the range 0..9.
+      uint8image: a [height, width, depth] uint8 Tensor with the image data
+  """
+
+  class CIFAR10Record(object):
+    pass
+  result = CIFAR10Record()
+
+  # Dimensions of the images in the CIFAR-10 dataset.
+  # See http://www.cs.toronto.edu/~kriz/cifar.html for a description of the
+  # input format.
+  label_bytes = 1  # 2 for CIFAR-100
+  result.height = 32
+  result.width = 32
+  result.depth = 3
+  image_bytes = result.height * result.width * result.depth
+  # Every record consists of a label followed by the image, with a
+  # fixed number of bytes for each.
+  record_bytes = label_bytes + image_bytes
+
+  # Read a record, getting filenames from the filename_queue.  No
+  # header or footer in the CIFAR-10 format, so we leave header_bytes
+  # and footer_bytes at their default of 0.
+  reader = tf.FixedLengthRecordReader(record_bytes=record_bytes)
+  result.key, value = reader.read(filename_queue)
+
+  # Convert from a string to a vector of uint8 that is record_bytes long.
+  record_bytes = tf.decode_raw(value, tf.uint8)
+
+  # The first bytes represent the label, which we convert from uint8->int32.
+  result.label = tf.cast(
+      tf.slice(record_bytes, [0], [label_bytes]), tf.int32)
+
+  # The remaining bytes after the label represent the image, which we reshape
+  # from [depth * height * width] to [depth, height, width].
+  depth_major = tf.reshape(tf.slice(record_bytes, [label_bytes], [image_bytes]),
+                           [result.depth, result.height, result.width])
+  # Convert from [depth, height, width] to [height, width, depth].
+  result.uint8image = tf.transpose(depth_major, [1, 2, 0])
+
+  return result