fix: resolve TypeError in BlockMinifloatQuantize backward pass

[Shreyas8612]

Description

This PR fixes a TypeError occurring during the backward pass of BlockMinifloatQuantize. The backward method was defined with additional positional arguments (width, exponent_width, etc.) that are not provided by the PyTorch autograd engine, leading to a signature mismatch.

Changes

• Removed unused positional arguments from BlockMinifloatQuantize.backward.
• Updated the return statement to match the expected number of gradients (returning None for the non-tensor inputs of the forward method).

Testing

Verified that the training/inference loop no longer crashes with:
`TypeError: BlockMinifloatQuantize.backward() missing 3 required positional arguments

[Copilot]

Pull request overview

This PR fixes a TypeError in the backward pass of BlockMinifloatQuantize by correcting the method signature. The backward method incorrectly defined additional positional arguments that PyTorch's autograd engine does not provide, causing crashes during training.

Changes:

• Removed incorrect positional arguments (width, exponent_width, exponent_bias_width, block_size, skip_first_dim) from the backward method signature
• Maintained the correct return statement with 6 values (gradient for input tensor + 5 None values for non-tensor parameters)

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