output[1, 0:2, :, ...] = input[1, 2:0:-1, :, ...]
output[2, 0:3, :, ...] = input[2, 3:0:-1, :, ...]
output[3, 0:5, :, ...] = input[3, 5:0:-1, :, ...]

# while entries past seq_lens are copied through:
output[0, 7:, :, ...] = input[0, 7:, :, ...]
output[1, 2:, :, ...] = input[1, 2:, :, ...]
output[2, 3:, :, ...] = input[2, 3:, :, ...]
output[3, 2:, :, ...] = input[3, 2:, :, ...]
```

          },
          "dataSourceRef": {
            "allOf": [
              {

                      items:
                        properties:
                          claim:
                            description: The name of the claim to be copied from.
                            type: string
                          header:
                            description: The name of the header to be created.
                            type: string

                      items:
                        properties:
                          claim:
                            description: The name of the claim to be copied from.
                            minLength: 1
                            type: string
                          header:
                            description: The name of the header to be created.

                      items:
                        properties:
                          claim:
                            description: The name of the claim to be copied from.
                            minLength: 1
                            type: string
                          header:
                            description: The name of the header to be created.

              }
            ],
            "default": {},
            "description": "ObjectMeta may contain labels and annotations that will be copied into the PVC when creating it. No other fields are allowed and will be rejected during validation."
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          "spec": {
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    *   When passing a `tf.mixed_precision.experimental.DynamicLossScale` to
        `tf.keras.mixed_precision.experimental.LossScaleOptimizer`, the weights
        of the `DynanmicLossScale` are copied into the `LossScaleOptimizer`
        instead of being reused. This means modifying the weights of the
        `DynamicLossScale` will no longer affect the weights of the
        LossScaleOptimizer, and vice versa.

Conic Sections, or other the simplest Figures with which they may be
compared. And some Years ago I lent out a Manuscript containing such
Theorems, and having since met with some Things copied out of it, I have
on this Occasion made it publick, prefixing to it an_ Introduction, _and
subjoining a_ Scholium _concerning that Method. And I have joined with
it another small Tract concerning the Curvilinear Figures of the Second