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Specifications and Common Mistakes

- Specifications and Common Mistakes:

- Misspellings or punctuation mistakes,incorrect formulas, abnormal display.

- Incorrect links, empty cells, or wrong formats.

- Chinese characters in English context.

- Minor inconsistencies between the UI and descriptions.

- Low writing fluency that does not affect understanding.

- Incorrect version numbers, including software package names and version numbers on the UI.

Usability

- Usability:

- Incorrect or missing key steps.

- Missing main function descriptions, keyword explanation, necessary prerequisites, or precautions.

- Ambiguous descriptions, unclear reference, or contradictory context.

- Unclear logic, such as missing classifications, items, and steps.

Correctness

- Correctness:

- Technical principles, function descriptions, supported platforms, parameter types, or exceptions inconsistent with that of software implementation.

- Incorrect schematic or architecture diagrams.

- Incorrect commands or command parameters.

- Incorrect code.

- Commands inconsistent with the functions.

- Wrong screenshots.

- Sample code running error, or running results inconsistent with the expectation.

Risk Warnings

- Risk Warnings:

- Lack of risk warnings for operations that may damage the system or important data.

Content Compliance

- Content Compliance:

- Contents that may violate applicable laws and regulations or geo-cultural context-sensitive words and expressions.

- Copyright infringement.

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mindflow.cell

API Name	Description	Supported Platforms
`mindflow.cell.FCSequential`	A sequential container of the dense layers, dense layers are added to the container sequentially.	`Ascend` `GPU`
`mindflow.cell.FNO1D`	The 1-dimensional Fourier Neural Operator (FNO1D) contains a lifting layer, multiple Fourier layers and a decoder layer.	`Ascend` `GPU`
`mindflow.cell.FNO2D`	The 2-dimensional Fourier Neural Operator (FNO2D) contains a lifting layer, multiple Fourier layers and a decoder layer.	`Ascend` `GPU`
`mindflow.cell.InputScale`	Scale the input value to specified region based on $(x_{i} - i n p u t_{c} e n t e r) * i n p u t_{s} c a l e$	`Ascend` `GPU`
`mindflow.cell.LinearBlock`	The LinearBlock.	`Ascend` `GPU`
`mindflow.cell.MultiScaleFCSequential`	The multi-scale fully conneted network.	`Ascend` `GPU`
`mindflow.cell.PDENet`	The PDE-Net model.	`Ascend` `GPU`
`mindflow.cell.ResBlock`	The ResBlock of dense layer.	`Ascend` `GPU`
`mindflow.cell.ViT`	This module based on ViT backbone which including encoder, decoding_embedding, decoder and dense layer.	`Ascend` `GPU`
`mindflow.cell.get_activation`	Gets the activation function.	`Ascend` `GPU`