[TF:TRT] Enable dynamic batch dim for Conv2dBackpropInput

[christopherbate]

This PR enables dynamic batch dimension for Conv2dBackpropInput.

We enable dynamic batch dimension for Conv2DBackpropInput simply by relaxing the check to allow dynamic batch dimension in the converter. We also add positive and negative tests.

The support for dynamic height and width will come later. This is because we will need to add an additional dynamic padding operation in order to support dynamic height and width for Conv2dBackpropinput.

[christopherbate]

@bixia1
@tfeher

[tfeher]

Thanks @christopherbate for this PR! I had a quick look at the changes, it looks good overall.

[bixia1]

This is what you have for the PR description:
This PR enables dynamic batch dimension for Conv2dBackpropInput.

Full dynamic shape for height and width will come later. We first need to add an additional dynamic padding operation support in order to handle the case where the input_shapes operation argument is non-constant.

Here is my suggestion to revise it. Do you see any problem?
This PR enables dynamic batch dimension for Conv2dBackpropInput.

We enable dynamic batch dimension for Conv2DBackpropInput simply by relaxing the check to allow dynamic batch dimension in the converter. We also add positive and negative tests.

The support for dynamic height and width will come later. This is because we will need to add an additional dynamic padding operation in order to support dynamic height and width for Conv2dBackpropinput.

[christopherbate]

This is what you have for the PR description:
This PR enables dynamic batch dimension for Conv2dBackpropInput.

Full dynamic shape for height and width will come later. We first need to add an additional dynamic padding operation support in order to handle the case where the input_shapes operation argument is non-constant.

Here is my suggestion to revise it. Do you see any problem?
This PR enables dynamic batch dimension for Conv2dBackpropInput.

We enable dynamic batch dimension for Conv2DBackpropInput simply by relaxing the check to allow dynamic batch dimension in the converter. We also add positive and negative tests.

The support for dynamic height and width will come later. This is because we will need to add an additional dynamic padding operation in order to support dynamic height and width for Conv2dBackpropinput.

No problem, I updated the description.

[christopherbate]

I updated test parameter naming to be inline with other dynamic shape developments.

I've rebased on master, but it still it shows difference between third_party/tf_runtime/workspace.bzl. Any ideas on why this is?