Help protect the Great Barrier Reef with TensorFlow on Kaggle

This operation pads a `tensor` according to the `paddings` you specify. `paddings` is an integer tensor with shape `[n, 2]`, where n is the rank of `tensor`. For each dimension D of `input`, `paddings[D, 0]` indicates how many values to add before the contents of `tensor` in that dimension, and `paddings[D, 1]` indicates how many values to add after the contents of `tensor` in that dimension. If `mode` is "REFLECT" then both `paddings[D, 0]` and `paddings[D, 1]` must be no greater than `tensor.dim_size(D) - 1`. If `mode` is "SYMMETRIC" then both `paddings[D, 0]` and `paddings[D, 1]` must be no greater than `tensor.dim_size(D)`.

The padded size of each dimension D of the output is:

`paddings[D, 0] + tensor.dim_size(D) + paddings[D, 1]`

#### For example:

``````t = tf.constant([[1, 2, 3], [4, 5, 6]])
paddings = tf.constant([[1, 1,], [2, 2]])
# 'constant_values' is 0.
# rank of 't' is 2.
#  [0, 0, 1, 2, 3, 0, 0],
#  [0, 0, 4, 5, 6, 0, 0],
#  [0, 0, 0, 0, 0, 0, 0]]

#  [3, 2, 1, 2, 3, 2, 1],
#  [6, 5, 4, 5, 6, 5, 4],
#  [3, 2, 1, 2, 3, 2, 1]]

#  [2, 1, 1, 2, 3, 3, 2],
#  [5, 4, 4, 5, 6, 6, 5],
#  [5, 4, 4, 5, 6, 6, 5]]
``````

`tensor` A `Tensor`.
`paddings` A `Tensor` of type `int32`.
`mode` One of "CONSTANT", "REFLECT", or "SYMMETRIC" (case-insensitive)
`constant_values` In "CONSTANT" mode, the scalar pad value to use. Must be same type as `tensor`.
`name` A name for the operation (optional).

A `Tensor`. Has the same type as `tensor`.

`ValueError` When mode is not one of "CONSTANT", "REFLECT", or "SYMMETRIC".

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